Abstract
Current models for long-term shoreline response to sea-level rise (SLR), such as the Bruun Rule, have significant limitations as they fail to account for site−specific processes and often misrepresent the influence of dunes, rocky platforms, or estuarine sediment sources and sinks. Here we integrate four previously published formulations into the Composite Shoreline−Retreat Workflow (CoShReW), a compound sequence that can be adapted or expanded according to data availability. The workflow needs several topo−bathymetric variables and shoreline data to calculate the expected shoreline retreat. It is distributed as open source code (GitHub) to guarantee reproducibility. As a first study-case, CoShReW is applied to 1281 cross-shore profiles along the highly heterogeneous Andalusian coast (SW Spain). To capture this coastal variability, we conduct a detailed characterization along the study area using Principal Component Analysis (PCA) of significant wave height, peak period, tidal range and sediment grain size, defining four different profile types. Resulting shoreline retreat projections differ by more than an order of magnitude among these classes, with dune elevation and estuary capacity emerging as dominant controls in dissipative sectors, and historical erosion rates prevailing on reflective or cliff−backed shores. Sensitivity analysis quantifies the relative importance of the governing variables for each profile type, indicating where local data collection would most improve forecast confidence. The present study findings reveal that local geomorphology can equal or exceed SLR as a driver of shoreline change in some habitats, emphasizing the need to consider and integrate site-specific adaptation measures in long-term shoreline retreat assessments for complex coastal environments.
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Garcia Tort, A., Gomes da Silva, P., Pellón, E., Cánovas, V., González, M., Medina, R., & da Fontoura Klein, A. H. (2025). Composite shoreline-retreat workflow (CoShReW): a case study on the Andalusian coast. Frontiers in Marine Science, 12. https://doi.org/10.3389/fmars.2025.1695289
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