Uncertainties in the Projected Patterns of Wave-Driven Longshore Sediment Transport Along a Non-straight Coastline

Abstract

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.

Uncertainties in wave-driven longshore sediment transport projections presented by a dynamic CMIP6-based ensemble

“…However, the northern beaches (with eastfacing coasts) are more exposed to offshore swell wave energy approaching the coast from all directions (Vieira Da Silva et al, 2018). The present study utilises the same modelling framework of (Zarifsanayei et al, 2022a, Zarifsanayei et al, 2022b, where seven sites along the GC shoreline were selected for investigation of uncertainty in the LST projections at sheltered (site B), semisheltered (sites A and C) and open coast locations (sites D to G; see Figure 3, which also shows the locations of offshore wave data buoys).…”

“…The correction methods are time-independent, modifying the general patterns of wave parameters represented in each quantile (statistical distribution). It has also been shown that applying bias correction techniques to offshore wave forcing conditions (i.e., GCM-Ws) is necessary to capture reasonable patterns for nearshore wave and sediment transport during the baseline period (Zarifsanayei et al, 2022b). Moreover, it was shown that the bias-corrected forcings preserve the signals of climate change presented by the original forcings.…”

“…It is important to note that uncertainty growth in the hindcast of LST patterns is relatively manageable through development of state-of-the-art strategies (e.g., better understanding of coastal processes, employing new generation models, using extensive benchmark data, calibration, etc.). However, narrowing uncertainty in the projection of LST patterns (i.e., future patterns of LST) is more challenging (Vitousek et al, 2021;Zarifsanayei et al, 2022b).…”

“…More recently, Zarifsanayei et al (2022b) quantified uncertainty in LST projections for a non-straight coastline (Gold Coast, Australia) using a more comprehensive sample of the uncertainty space. They developed two ensembles, formed by original and biascorrected wave datasets of CSIRO CMIP5 (eight sets of GCM-Ws) projected under two emission scenarios, a hybrid wavetransformation method, and eight sediment transport models (including both bulk formula and process-based models).…”

“…Moreover, the inclusion of the influence of Sea Level Rise (SLR) on wave transformation, where nearshore bathymetry is relatively complex, might change the wave dissipation patterns and the resulting LST patterns. This study aims to address these knowledge gaps using experiments developed using newly available CMIP6-driven wave simulations with the Gold Coast, Australia selected as the study site (Zarifsanayei et al, 2022a;2022b). In particular, the experiments were designed to investigate the following aspects of uncertainty:…”