In this study four experiments were conducted to investigate uncertainty in future longshore sediment transport (LST) projections due to: working with continuous time series of CSIRO CMIP6-driven waves (experiment #1) or sliced time series of waves from CSIRO-CMIP6-Ws and CSIRO-CMIP5-Ws (experiment #2); different wave-model-parametrization pairs to generate wave projections (experiment #3); and the inclusion/exclusion of sea level rise (SLR) for wave transformation (experiment #4). For each experiment, a weighted ensemble consisting of offshore wave forcing conditions, a surrogate model for nearshore wave transformation and eight LST models was used. The results of experiment # 1 indicated that the annual LST rates obtained from a continuous time series of waves were influenced by climate variability acting on timescales of 20-30 years. Uncertainty decomposition clearly reveals that for near-future coastal planning, a large part of the uncertainty arises from model selection and natural variability of the system (e.g., on average, 4% scenario, 57% model, and 39% internal variability). For the far future, the total uncertainty consists of 25% scenario, 54% model and 21% internal variability. Experiment #2 indicates that CMIP6 driven wave climatology yield similar outcomes to CMIP5 driven wave climatology in that LST rates decrease along the study area’s coast by less than 10%. The results of experiment #3 indicate that intra- and inter-annual variability of LST rates are influenced by the parameterization schemes of the wave simulations. This can increase the range of uncertainty in the LST projections and at the same time can limit the robustness of the projections. The inclusion of SLR (experiment #4) in wave transformation, under SSP1-2.6 and SSP5-8.5 scenarios, yields only meagre changes in the LST projections, compared to the case no SLR. However, it is noted that future research on SLR influence should include potential changes in nearshore profile shapes.

Although there have been many efforts in the literature to hindcast the patterns of longshore sediment transport (LST), they mainly disregarded uncertainty issues. Forcing datasets, wave transformation methods, and LST models are among the main sources of uncertainty in LST estimations. The combination of the aforementioned sources of uncertainty makes the estimation of LST patterns challenging for non-straight coastlines as the uncertainty ranges might vary from site to site. In this paper, a simple ensemble modeling framework was employed to investigate LST rate uncertainty at seven sites along a non-straight coastline (Gold Coast, Australia). The ensemble was formed by two different forcing datasets (i.e., integral parameters of total wave energy from two hindcast datasets of ERA5 and CAWCR), two different wave transformation methods (i.e., inclusion and exclusion of local wind effects), and eight LST models (i.e., bulk formulae and process-based models). Moreover, the relative importance of each source of uncertainty was ranked using the ANOVA-variance-based model. Finally, the weighted ensemble mean was used to investigate intra- and inter-annual variability of LST rates. The results showed that the range of uncertainty of LST rates for open coasts of Gold Coast is much higher than that of semi-sheltered coasts. On annual scale, for open coasts, 40% to 50% of total uncertainty was due to the choice of wave transformation methods, while for semi-sheltered coasts, it was 20%–30%. Moreover, almost for all sites, 30% to 50% of total uncertainty was controlled by the choice of LST models and the interaction of wave transformation methods and LST models. Although the weighted ensemble mean could provide an estimate of LST patterns along the coast, addressing the residual uncertainties (arising from other sources, discussed at the end of this paper), in future works, would help increase the certainty of the estimations.

This study quantifies the uncertainties in the projected changes in potential longshore sediment transport (LST) rates along a non-straight coastline. Four main sources of uncertainty, including the choice of emission scenarios, Global Circulation Model-driven offshore wave datasets (GCM-Ws), LST models, and their non-linear interactions were addressed through two ensemble modelling frameworks. The first ensemble consisted of the offshore wave forcing conditions without any bias correction (i.e., wave parameters extracted from eight datasets of GCM-Ws for baseline period 1979–2005, and future period 2081–2100 under two emission scenarios), a hybrid wave transformation method, and eight LST models (i.e., four bulk formulae, four process-based models). The differentiating factor of the second ensemble was the application of bias correction to the GCM-Ws, using a hindcast dataset as the reference. All ensemble members were weighted according to their performance to reproduce the reference LST patterns for the baseline period. Additionally, the total uncertainty of the LST projections was decomposed into the main sources and their interactions using the ANOVA method. Finally, the robustness of the LST projections was checked. Comparison of the projected changes in LST rates obtained from two ensembles indicated that the bias correction could relatively reduce the ranges of the uncertainty in the LST projections. On the annual scale, the contribution of emission scenarios, GCM-Ws, LST models and non-linear interactions to the total uncertainty was about 10–20, 35–50, 5–15, and 30–35%, respectively. Overall, the weighted means of the ensembles reported a decrease in net annual mean LST rates (less than 10% under RCP 4.5, a 10–20% under RCP 8.5). However, no robust projected changes in LST rates on annual and seasonal scales were found, questioning any ultimate decision being made using the means of the projected changes.