Scalable, data-assimilated models predict large-scale shoreline response to waves and sea-level rise

Scalable, data-assimilated models predict large-scale shoreline response to waves and sea-level rise

Blog Article

Abstract Coastal change is a complex combination of multi-scale processes (e.g., wave-driven cross-shore and longshore transport; dune, bluff, and cliff erosion; overwash; fluvial and inlet sediment supply; and sea-level-driven recession).

Historical Dresses sea-level-driven coastal recession on open ocean coasts is often outpaced by wave-driven change.However, future sea-level-driven coastal recession is expected to increase significantly in tandem with accelerating rates of global sea-level rise.Few models of coastal sediment transport can resolve the multitude of coastal-change processes at a given beach, and fewer still are computationally efficient enough to achieve large-scale, long-term simulations, while accounting for historical behavior and uncertainties in future climate.

Here, we show that a scalable, data-assimilated shoreline-change model can achieve realistic simulations of long-term coastal change and uncertainty across large coastal regions.As part of the modeling case study of the U.S.

South Atlantic Coast (Miami, Florida to Delaware Bay) presented here, L-Citrulline we apply historical, satellite-derived observations of shoreline position combined with daily hindcasted and projected wave and sea-level conditions to estimate long-term coastal change by 2100.We find that 63 to 94% of the shorelines on the U.S.

South Atlantic Coast are projected to retreat past the present-day extent of sandy beach under 1.0 to 2.0 m of sea-level rise, respectively, without large-scale interventions.