Predicting shoreline response to sea level rise along the varying coastline of Scania, southern Sweden

Sea level rise is a key effect of modern climate change. The ~500 km-long Scania coastline is uniquely sensitive within Sweden to sea level rise (SLR) because it is mostly formed of sediments and its low-lying topography hosts extensive built environments. Swedish municipalities are legally obliged to include climate- and erosion-related risks in their spatial planning. However, it is a challenge for coastal municipalities to fully comply with the legal requirements because the scientific basis is currently difficult to interpret for coastal adaptation to sea level rise and there is still limited research describing the effects of sea-level rise on erosion along Swedish coasts. Our research is addressing these shortcomings through: (i) developing physical process-based predictions of erosion driven by sea level rise for the Scania coastline; and (ii) alongside end-users, co-creating an understanding of how erosion predictions are best communicated to make them accessible, actionable and relevant from the end-user’s perspective. Key challenges to predicting coastal response to sea level rise in southern Sweden include complex topography, varying wave and sediment conditions, and limited material supply. Because of shortcomings in the scientific understanding of SLR-driven coastal erosion, we take an ensemble approach that combines deterministic and probabilistic methods. Our preliminary modelling has focused on translation of equilibrium profiles of different shapes to estimate erosion. We find that a gradual increase in sea level, where the translated profile at the previous time step is used as input to the next step, induces more erosion than an instantaneous shift over the total sea level rise, because more material is deposited in the offshore during this iterative procedure. Our modelling of shoreline responses is being further developed and predictions of coastal response to sea level rise, including analyses of probabilities, will be communicated to our end-users using a GIS platform.