HOW IMPORTANT ARE SEDIMENT BUDGET ESTIMATES FOR SHORELINE PROJECTIONS UNDER RISING SEA LEVELS?
Description
This study examines coastal dynamics along the central-southern coast of Rio Grande do Sul (RS), focusing on the interaction between the sediment budget (SB) and sea-level rise (SLR). Using the RanSTM model, the morphological response of the region to SLR was simulated. Calibration employed SB data estimated at different temporal scales: annual (DRONE surveys), decadal (satellite imagery), and long-term (stratigraphy and geophysics).
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Steps to reproduce
This study applied the RanSTM (Random Shoreface Translation Model) (Cowell et al., 2006), a stochastic extension of the STM (Cowell et al., 1992), to simulate shoreline evolution in response to sea-level rise and sediment budget variations. RanSTM allows each input parameter to assume up to three values (min, mode, max), represented as probability density functions (PDFs) and sampled in Monte Carlo simulations, thus incorporating data uncertainty (e.g., sea-level rise projections). Model inputs include sea-level variation, sediment budget, and substrate characteristics (geometry and texture). Three key elements were required to build the input dataset: (i) topobathymetric profiles (onshore and offshore morphology within active limits), (ii) sediment budget estimates, and (iii) sea-level variation. The study area was divided into four coastal cells, following the coastal-tract methodology of Cowell et al. (2003). A total of 16 simulations were performed to estimate future coastline positions, combining sediment budget and sea level rise. Sediment Budget Data Sources: Satellite-derived balance (SAT), large-scale balance (LE), and drone-monitoring balance (DRONE). Simulation Methodology: For each simulation, only the sediment budget input parameters varied, while other inputs (sea level rise, substrate morphology, and shoreface geometry) were kept constant. This ensured that changes in recession distance solely reflected the effects of different sediment budget weightings. To understand the influence of sediment budget (SB) and sea level rise (SLR), 32 deterministic simulations were conducted (4 cells; 4 temporal horizons: 2040, 2050, 2075, and 2125; 2 variables: SB and SLR). For SB influence, SLR was kept at zero (stable sea level), and vice-versa for SLR influence.
Institutions
- Universidade Federal do Rio Grande