Abstract
Abstract. This paper presents a methodology that uses site-specific topographic and cosmogenic 10Be data to perform multi-objective model optimisation of a coupled coastal evolution and cosmogenic radionuclide production model. Optimal parameter estimation of the coupled model minimises discrepancies between model simulations and measured data to reveal the most likely history of rock coast development. This new capability allows a time series of cliff retreat rates to be quantified for rock coast sites over millennial timescales. Without such methods, long-term cliff retreat cannot be understood well, as historical records only cover the past ∼150 years. This is the first study that has (1) applied a process-based coastal evolution model to quantify long-term cliff retreat rates for real rock coast sites and (2) coupled cosmogenic radionuclide analysis with a process-based model. The Dakota optimisation software toolkit is used as an interface between the coupled coastal evolution and cosmogenic radionuclide production model and optimisation libraries. This framework enables future applications of datasets associated with a range of rock coast settings to be explored. Process-based coastal evolution models simplify erosional processes and, as a result, often have equifinality properties, for example that similar topography develops via different evolutionary trajectories. Our results show that coupling modelled topography with modelled 10Be concentrations can reduce equifinality in model outputs. Furthermore, our results reveal that multi-objective optimisation is essential in limiting model equifinality caused by parameter correlation to constrain best-fit model results for real-world sites. Results from two UK sites indicate that the rates of cliff retreat over millennial timescales are primarily driven by the rates of relative sea level rise. These findings provide strong motivation for further studies that investigate the effect of past and future relative sea level rise on cliff retreat at other rock coast sites globally.
Highlights
Fundamental features of a rock coast are a sea cliff and shore platform, and the rate of cliff retreat is foremost the collective result of processes eroding the cliff face horizontally and the shore platform vertically (Sunamura, 1992; Trenhaile, 2008a)
The profile is extracted from a high-resolution digital surface model (DSM) generated by structure-from-motion analysis of aerial photographs collected by an unmanned aerial vehicle (UAV) survey at both sites
The Markov chain Monte Carlo (MCMC) best-fit result with 95 % weighting assigned to the 10Be concentration profile and 5 % assigned to the topographic profile results in a topographic profile that is considerably steeper than the measured profile at Bideford
Summary
Fundamental features of a rock coast are a sea cliff and shore platform, and the rate of cliff retreat is foremost the collective result of processes eroding the cliff face horizontally and the shore platform vertically (Sunamura, 1992; Trenhaile, 2008a). The ability to erode a cliff face depends fundamentally on the type of cliff material exposed to the delivery of energy to the cliff surface, usually in the form of waves. Delivery of wave energy is mediated by the configuration of the shore platform, beach width, and wave climate (Sunamura, 1992). Shadrick et al.: Multi-objective optimisation of a rock coast evolution model al., 2020; Naylor and Stephenson, 2010; Prémaillon et al, 2018; Thompson et al, 2019). These complex and varied processes make predicting long-term cliff erosion rates difficult. With climate change threatening the stability of these coastlines through RSL rise and increased storminess (Trenhaile, 2014), accurate long-term predictions of erosion rates will be highly valuable in the development of scenarios within the context of coastal management
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have