Estuaries and coasts can be conceptualized as connected networks of water and sediment fluxes. These dynamic geomorphic systems are governed by waves, tides, wind, and river input, and evolve according to complex nonlinear transport processes. To predict their evolution, we need
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Estuaries and coasts can be conceptualized as connected networks of water and sediment fluxes. These dynamic geomorphic systems are governed by waves, tides, wind, and river input, and evolve according to complex nonlinear transport processes. To predict their evolution, we need to better understand the pathways that sediment takes from source through temporary storage areas to sink. Knowledge of these pathways is essential for predicting the response of such systems to climate change impacts or human interventions (e.g., dredging and nourishment). The conceptual framework of sediment connectivity has the potential to expand our system understanding and address practical coastal management problems (Pearson et al., 2020). Connectivity provides a structured framework for analyzing these sediment pathways, schematizing the system as a series of geomorphic cells or nodes, and the sediment fluxes between those nodes as links (Heckmann et al., 2015). Once organized in this fashion, the resulting network can be expressed algebraically as an adjacency matrix: sediment moving from a given source to different receptors. There is a wealth of pre-existing statistical tools and techniques that can be used to interpret the data once it is in this form, drawing on developments in other scientific disciplines (Newman, 2018; Rubinov & Sporns, 2010). Lagrangian flow networks have been increasingly used to analyze flow and transport pathways in oceanographic and geophysical applications (Padberg-Gehle & Schneide, 2017; Reijnders et al., 2021; Ser-Giacomi et al., 2015). However, this approach has not yet been adopted to analyze coastal or estuarine sediment transport, and requires a multitude of field measurements or numerical model simulations. Lagrangian particle tracking has been widely used to assess connectivity in the context of oceanography and marine ecology (Hufnagl et al., 2016; van Sebille et al., 2018), because the models record the complete history of a particle’s trajectory, not only its start and end points. Particle tracking models are also relatively fast and lend themselves well to parallel computing (Paris et al., 2013). This approach thus permits a faster and more detailed analysis of sediment connectivity than existing Eulerian approaches (e.g., Pearson et al., (2020)). Although several Lagrangian sediment transport models have been developed (e.g., (MacDonald & Davies, 2007; Soulsby et al., 2011)), they have not been used to support connectivity studies. Hence, there is a need for Lagrangian sediment particle tracking tools tailored to predicting sediment transport pathways and determining connectivity of complex coastal systems. To meet this need, we developed a Lagrangian sediment transport model, SedTRAILS (Sediment TRAnsport vIsualization & Lagrangian Simulator) and used it to develop a sediment connectivity network. Our approach provides new analytical techniques for distilling relevant patterns from the chaotic, spaghetti-like network of sediment pathways that often characterize estuarine and coastal systems. We demonstrate a proof of concept for our approach by applying it to a case using these tools. @en
