Prediction of shoreline–shelf depositional process regime guided by palaeotidal modelling

Ancient shoreline–shelf depositional systems are influenced by an unusually wide array of geological, biological and hydrodynamic processes, with sediment transport deposition primarily determined the interaction river, wave (including storm) tidal changes in relative sea level. Understanding impact these processes on morphodynamics stratigraphic preservation remains challenging. Numerical modelling integrated traditional facies analysis provides increasingly viable approach, potential to quantify, thereby improve understanding of, complex coastal sedimentary processes. An approach is presented here that focuses palaeotidal investigate controls ancient tides their influence – one three cornerstones ternary process classification scheme shoreline-shelf systems. methodology reviewed illustrated model case studies different scales focus. The results synthesised context including a critique modification process-based scheme. emphasis reflects global importance throughout Earth’s history. models able highlight quantify following four processes: (1) physiography (shape depth) oceans (1000s km scale) determines degree resonance; (2) ocean connections partly enclosed water bodies (100–1000s regional-scale flux energy (inflow versus outflow); (3) continental shelves influences shelf resonance potential; (4) relatively local-scale embayments (typically 1–10s balance amplification due funnelling, shoaling effects frictional damping. In deep time, palaeogeographic palaeobathymetric uncertainty can be accounted for performing sensitivity analyses scenarios, across this range spatial scales. These incorporated into updated predictive decision tree determining regime terms wave, fluvial considers basin physiography, width shoreline morphology separately. Uncertainty ambiguity applying widely used three-tier reduced using conjunction proposed two-tier limited primary secondary This studies, showing how integration numerical preserved record improves confidence prediction tide-influenced regimes. Wider application will further classifications predictions modern