Uncertainties in Shoreline Projections to 2100 at Truc Vert beach (France): Role of Sea-Level Rise and Equilibrium Model Assumptions

This data is used to produce ensemble shoreline projections through 2020-2100 at Truc Vert beach (France), using state-of-the-art Sea-level rise (SLR) and wave projections (data shared here), and two different equilibrium shoreline models. Uncertainties in shoreline projections are analysed with a Global Sensitivity Analysis to identify the main source of uncertainty among the stochastic model inputs (SLR, model free parameters, and depth of closure) over time. We found that uncertainties in shoreline projections are dominated by the uncertainty in model free parameters over the first half of the 21st century, while the dominance of SLR uncertainties only emerges in the second half of the century. We also find that the effects of uncertainties in model parameters as well as the choice of modelling approach are dependent on wave climate variability, and it is therefore critical to include uncertainties in wave climate variability. SLR projections are assessed using median and likely range (17th-83rd percentiles) of SROCC projections, considering regional fingerprints of the several contributors, following (Thiéblemont et al., 2019). SONEL for past sea levels and vertical land motion data (https://www.sonel.org/-GPS-.html). Wave hindcast data, used for model calibration and correction of future wave data, is provided by LOPS-Ifremer NORGAS-UG regional model (Michaud et al., 2016). 1970-2005 and 2020-2100 wave time series are provided by Bricheno & Wolf (2018) Nort-East Atlantic regional model historic and projections runs. Wave projections are corrected with a quantile-quantile method using historic Bricheno & Wolf (2018) data and NORGASUG hindcast. The ShoreFor (Splinter et al., 2014) and Yates et al. (2009) shoreline models were calibrated using shoreline data derived from longshore-averaged topographic surveys, NORGAS-UG hindcast data, reconstructed past mean sea level, and the Simulated Annealing algorithm (Bertsimas & Tsitsiklis, 1993).