A Simple Predictive Model for Salt Marsh Internal Deterioration Under Sea-Level Rise and Sediment Deficits: Application to Chesapeake Bay

Abstract

Salt marshes are dynamic biogeomorphic systems reliant on autochthonous and allochthonous input to maintain their three-dimensional configuration. Sea-level rise, subsidence, and sediment deficits can lead to submergence, open-water expansion, and ultimately loss of the vegetated marsh plain and associated ecosystem services. Widely used management-focused models focus on vegetation zonation in response to sea level but neglect sediment transport processes and geomorphic change. Process-based research models attempt to represent complex physical and biogeomorphic interactions but operate on spatiotemporal scales that are not directly transferable to restoration or management. Here we bridge these two paradigms and present a novel geomorphic model (UBMorph) based on the sediment-based lifespan concept that accounts for sea-level rise and open-water expansion to predict changes in salt marsh area in Chesapeake Bay. Model parameters such as surface accretion rate and elevation-to-areal loss fraction are selected using a separate, fully coupled biogeomorphic model (MarshMorpho2D) and the predicted lifespan is then compared with high marsh coverage from a zonation model (SLAMM). Across all of Chesapeake Bay, UBMorph estimates an overall loss of 404 km 2 (37%) of vegetated marsh area under a dynamic 3–12 mm/y sea-level rise scenario (between 2010 and 2110). We then demonstrate a management-focused application of UBMorph and SLAMM used in tandem, for developing both a marsh condition and restoration model of the Chesapeake Bay portion of Maryland. The restoration model, which includes hydrologic intervention and sediment placement actions, indicates that ~ 400 km 2 of marsh require either no intervention or low effort hydrologic intervention presently, whereas if no action is taken, over 700 km 2 will require high effort intervention by 2070. This synthesis of research models with management-focused decision models demonstrates a tangible advance in bridging the gap between process-based research and restoration needs.

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