A rules-based shoreface translation and sediment budgeting tool for estimating coastal change: ShoreTrans

Predicting change to shorelines globally presents an increasing challenge as sea level rise (SLR) accelerates. Many shoreline prediction models use the simplistic ‘Bruun rule’ for dealing with SLR profile translation, in-part due alternative approaches being too complex and time-consuming implement. To address this, we introduce ShoreTrans: a simple, rules-based, user-input driven, shoreface translation sediment budgeting model, that applies surveyed 2D-profile (not parameterization), estimating realistic coastlines, resulting from variations in supply, while accounting armouring, hard-rock cliffs outcropping rocks. The tool can be applied sand, gravel, rock engineered coasts at temporal scale of 10–100 years, trends well variability. method accounts for: (1) dune encroachment/accretion; (2) barrier rollback; (3) non-erodible layers; (4) seawalls; (5) lower transport; (6) alongshore rotation; (7) other sources sinks. Uncertainty is accounted using probabilistic distribution inputs Monte Carlo simulations. We provide first-pass assessment two macrotidal UK embayments: Perranporth (sandy, dissipative, cross-shore dominant transport) Start Bay (gravel, reflective, bi-directional dominant), then idealised profiles investigate relative importance forcing controls on recession beach width. For dissipative sandy site, primary modes coastal are predicted short-term storm erosion long-term may important but highly uncertain. reflective gravel mode multi-decadal longshore flux, rotation secondary. Relative ShoreTrans approach, Bruun rule under-predicts front cliffs, seawalls low barriers rollback, over-predicts where large erodible dunes present. directly addresses width, beaches shrink, such narrow (<50 m width) disappear under 1-m SLR. As standalone tool, transferable many coast types will practitioners simple estimate how 2D appearance A future benefit combine this approach existing hybrid modelling techniques augment predictions.