Coastal tidal estuaries are vital to the exchange of energy and material between inland waters and the open ocean.Debris originating from the land and ocean enter this environment and are transported by currents (river outflow and tide), wind, waves and density gradients. Understanding and predicting the source and fate of such debris has considerable environmental, economic and visual importance. We show that this issue can be addressed using the Lagrangian coherent structures (LCS) technique which is highly robust to hydrodynamic model uncertainties.Here we present a comprehensive study showing the utility of this approach to describe the fate of floating material in a coastal tidal embayment. An example is given from Moreton Bay, a semi-enclosed subtropical embayment with high morphologic, ecological and economic significance to Southeast Queensland, Australia. Transport barriers visualised by the LCS create pathways and barriers for material transport in the embayment. It was found that the wind field modified both the rate attraction and location of the transport barriers. One of the key outcomes is the demonstration of the significant role of islands in partitioning the transport of material and mixing within the embayment. The distribution of the debris sources along the shoreline are explained by the relative location of the LCS to the shoreline. Therefore, extraction of LCS can help to predict sources and fate of anthropogenic marine debris and thus, serve as a useful way for effective management of vulnerable regions and marine protected areas.

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Deployment of Lagrangian drifters in water systems can provide a larger spatial coverage and an additional insight into horizontal motion of particles than Eulerian techniques. This feature has provided an opportunity to assimilate Lagrangian data into hydrodynamic models to enhance their accuracies. Numerical models suffer from both systematic and random errors. Conventional data assimilation methods were designed to reduce the stochastic errors, and systematic errors can negatively affect the assimilation systems. Therefore, a calibration process, which is an effective way to reduce systematic errors and consequently biases in the numerical models, is required to be performed before implementation of data assimilation techniques. In this study, D-Flow FM, a hydrodynamic model, was set up for simulating the essential processes in a micro-tidal estuary in Queensland, Australia. To calibrate the model, bathymetry and bed roughness were selected as calibration parameters, while most studies in estuarine application have only considered the bed roughness as the calibration parameter. Evaluation of model performance in terms of correlation and root mean square error between model outputs and observations for both water level and velocity showed that calibration of bathymetry is important. Herein model outputs are validated with Lagrangian drifter velocity data for different environmental conditions. The results showed that calibration with the consideration of bed roughness and bathymetry reduced the systematic errors and increase the correlation between model outputs and Lagrangian drifter data. This is an important step prior to assimilation of Lagrangian data to reduce stochastic errors.

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