Modelling Cross-Shore Shoreline Change on Multiple Timescales and Their Interactions

Abstract

In this paper, a new approach to model wave-driven, cross-shore shoreline change incorporating multiple timescales is introduced. As a base, we use the equilibrium shoreline prediction model ShoreFor that accounts for a single timescale only. High-resolution shoreline data collected at three distinctly different study sites is used to train the new data-driven model. In addition to the direct forcing approach used in most models, here two additional terms are introduced: a time-upscaling and a time-downscaling term. The upscaling term accounts for the persistent effect of short-term events, such as storms, on the shoreline position. The downscaling term accounts for the effect of long-term shoreline modulations, caused by, for example, climate variability, on shorter event impacts. The multi-timescale model shows improvement compared to the original ShoreFor model (a normalized mean square error improvement during validation of 18 to 59%) at the three contrasted sandy beaches. Moreover, it gains insight in the various timescales (storms to inter-annual) and reveals their interactions that cause shoreline change. We find that extreme forcing events have a persistent shoreline impact and cause 57–73% of the shoreline variability at the three sites. Moreover, long-term shoreline trends affect short-term forcing event impacts and determine 20–27% of the shoreline variability.

Combined Longshore and Cross-Shore Modeling for Low-Energy Embayed Sandy Beaches

“…The relatively short time scale of 20 days encompasses that of winter monsoon pulses [28], on which the model can now focus more optimally. This is in line with the multi-scale approach of ShoreFor developed in [17,41], but within a simpler formulation. Figure 11c also shows that the seasonal fluctuation of the longshore contribution is now more pronounced than in the previous calibration (Figure 10c).…”

“…During summer monsoons, the small wave forcing leads to a small Ω eq , very similar to the instantaneous Dean number Ω, so that the disequilibrium Dean number is small and induces a small shoreline variation. This suggests that an intra-seasonal memory decay is not appropriate for summer conditions and a multi-scale approach should be considered [17,41]. Alternatively, here, the definition of Ω eq is re-valuated for summer.…”

“…The success of cross-shore models relies on the timescale used to correct the disequilibrium of shoreline position under a particular forcing. To improve its predictive skills, the work in [17] uses a multi-timescale approach, which accounts for storms and seasonal and inter-annual wave climate variability, including the scale interactions between these different forcings.…”

Challenges and Opportunities in Coastal Shoreline Prediction

“…For example, Ibaceta et al (2020) suggested an ensemble Kalman Filter assimilation technique could track changes in model structure due to changing wave-shoreline feedback. Montaño et al (2021) and Schepper et al (2021) separately proposed two different multi-scale model approaches. Montaño et al (2021) used a Complete Ensemble Empirical Mode Decomposition method that linked different timescales of forcing to the corresponding timescales in the shoreline change.…”

“…Montaño et al (2021) used a Complete Ensemble Empirical Mode Decomposition method that linked different timescales of forcing to the corresponding timescales in the shoreline change. In contrast, Schepper et al (2021) included upscaling, downscaling and direct forcing methods to link forcing to various timescales of shoreline evolution. These advances hint at ways forward to robustly predict future shoreline changes in a changing and uncertain climate using these simplified reduced complexity models.…”