Evan Goldstein | Geology Reports

USGS2020

Impacts of seagrass dynamics on the coupled long-term evolution of barrier-marsh-bay systems

Seagrass provides a wide range of economically and ecologically valuable ecosystem services, with shoreline erosion control often listed as a key service, but can also alter the sediment dynamics and waves within back-barrier bays. Here we incorporate seagrass dynamics into an existing barrier-marsh exploratory model, GEOMBEST++, to examine the coupled interactions of the back-barrier bay with both adjacent (marsh) and non-adjacent (barrier island) subsystems. While seagrass reduces marsh edge erosion rates and increases progradation rates in many of our 288 model simulations, seagrass surprisingly increases marsh edge erosion rates when sediment export from the back-barrier basin is negligible because the ability of seagrass to reduce the volume of marsh sediment eroded matters little for back-barrier basins in which all sediment is conserved. Our model simulations also suggest that adding seagrass to the bay subsystem leads to increased deposition in the bay, reduced sediment available to the marsh, and enhanced marsh edge erosion until the bay reaches a new, shallower equilibrium depth. In contrast, removing seagrass liberates previously-sequestered sediment that is then delivered to the marsh, leading to enhanced marsh progradation. Lastly, we find that seagrass reduces barrier island migration rates in the absence of back-barrier marsh by filling accommodation space in the bay. These model observations suggest that seagrass meadows operate as dynamic sources and sinks of sediment that can influence the evolution of coupled marsh and barrier island landforms in unanticipated ways.

Virginia↗

USGS2019

A review of machine learning applications to coastal sediment transport and morphodynamics

A range of computer science methods under the heading of machine learning (ML) enables the extraction of insight and quantitative relationships from multidimensional datasets. Here, we review some common ML methods and their application to studies of coastal morphodynamics and sediment transport. We examine aspects of ‘what’ and ‘why’ ML methods contribute, such as ‘what’ science problems ML tools have been used to address, ‘what’ was learned when using ML, and ‘why’ authors used ML methods. We find a variety of research questions have been addressed, ranging from small-scale predictions of sediment transport to larger-scale sand bar morphodynamics and coastal overwash on a developed island. We find various reasons justify the use of ML, including maximize predictability, emulation of model components, smooth and continuous nonlinear regression through data, and explicit inclusion of uncertainty. Overall the expanding use of ML has allowed for an expanding set of questions to be addressed. After reviewing the studies we outline a set of ‘best practices’ for coastal researchers using machine learning methods. Finally we suggest possible areas for future research, including the use of novel machine learning techniques and exploring ‘open data’ that is becoming increasingly available.

Earth-Science Reviews↗