Modelling the future evolution of incised coastal gullies using a coupled terrestrial-coastal landscape evolution model

Numerical models of landscape evolution provide powerful tools to assess the impacts that changes in driving forces may have on a landscape. Historically, models of coastal- and terrestrial-landscape evolution have been kept separate. Considering that the driving forces of coastal and terrestrial erosion are projected to change under future climate change scenarios, it is somewhat surprising that the impacts of these changes and the interaction between terrestrial and coastal processes has yet to be fully explored within a numerical modelling framework. In this paper we present a modified version of the Channel-Hillslope Integrated Landscape Development (CHILD) model which incorporates a new module describing the retreat of coastal soft-cliffs. This new cliff retreat model is founded on the underlying premise that wave energy is the key factor driving soft cliff erosion. Specifically, we assume that the rate of cliff retreat (in any year) is a function of the accumulated wave energy above a threshold. Since it is driven by wave energy regimes, the cliff retreat model is well suited for impacts analyses using future wave climates developed by modifying the statistical properties of baseline wave spectra in accordance with climate change predictions. This allows straightforward analysis of the impacts not only in terms of altered wave heights (including adjustments forced by seasonal tides, surges and longer-term sea-level rise), but also changes in storm intensity, duration and inter-storm period. In this paper we report the findings from a validation study which compares the predictions from the coupled terrestrial-coastal CHILD model with data from a series of incised coastal gullies (known locally as 'Chines'), found along the south west coast of the Isle of Wight, UK. These features are ideally suited to this study as their evolution is controlled by both terrestrial and coastal processes; namely rates of knickpoint recession and cliff retreat. Chines are highly dynamic features, responding to changes in either of the aforementioned processes.