The Teignmouth Model: Validation and evaluation of Delft3D-MOR with COAST3D Pilot campaign data

Computer models are commonly used to simulate the behaviour of the coast in response to natural processes (e.g. storms, extreme sea levels) or management plans (e.g. beach nourishment, channel dredging). The COAST3D project was initiated to improve and validate these numerical models. COAST3D stands for Coastal Study of ThreeDimensional Sand Transport Processes and Morphodynamics. In March 1999, a COAST3D Pilot experiment was held in Teignmouth on the coast of Devon in Southwest England.Teignmouth has a very irregular coastline. It comprises a tidal inlet adjacent to the beach and a sandstone cliff, making it three-dimensional.This study involves the modelling of the Teignmouth site with data from the Pilot campaign. The general goal is to validate Delft3D-MOR against the field data taken in Teignmouth. For this purpose, a model grid covering the Teignmouth site is constructed. The hydrodynamic boundary conditions are determined by nesting the Teignmouth grid into a larger model, the Lyme Bay Model, which is in turn nested into the Continental Shelf Model. After the nesting procedure, the boundary conditions are calibrated with the use of water levels recorded during the Pilot campaign. The influence of the Teignmouth estuary is calibrated by varying the bed roughness inside the estuary. However, the tidal flow through the estuary mouth can not be modelled accurately, as the bathymetry data used in the Teignmouth model is outdated. The different COAST3D modelling teams agreed to carry out three common test cases: 1) A spring tidal cycle without waves. 2) A hypothetical situation with high waves and a fixed water level. 3) A neap tidal cycle with large waves. The focus of this study is also on these three test cases.The suspended and bedload transports and resulting bed-level changes are also computed for each test case. This is done with and without intratidal bed updating.Civil Engineering and Geoscience

Quasi-3D modelling of surf zone dynamics

A quasi-three-dimensional model (quasi-3D) has been developed through the implementation of an analytical 1DV flow model in existing depth-averaged shallow water equations. The model includes the effects of waves and wind on the vertical distribution of the horizontal velocities. Comparisons with data from both physical and field cases show that the quasi-3D approach is able to combine the effect of vertical structures with the efficiency of depth-averaged simulations. Inter-comparisons with three-dimensional simulations show that the quasi-3D approach can represent similar velocity profiles in the surf zone. Quasi-3D morphodynamic simulations show that the bed dynamics in the surf zone represent the relevant 3D effects in the surf zone much more than the depth-averaged computations. It was shown that the quasi-3D approach is computationally efficient as it only adds about 15-20% to the runtimes of a 2DH simulation which is minor compared to a run time increase of 250-800% when switching to a 3D simulationHydraulic EngineeringCivil Engineering and Geoscience

Morphological impact of a storm can be predicted three days ahead

People living behind coastal dunes depend on the strength and resilience of dunes for their safety. Forecasts of hydrodynamic conditions and morphological change on a timescale of several days can provide essential information to protect lives and property. In order for forecasts to protect they need be relevant, accurate, provide lead time, and information on confidence. Here we show how confident one can be in morphological predictions of several days ahead. The question is answered by assessing the forecast skill as a function of lead time. The study site in the town of Egmond, the Netherlands, where people depend on the dunes for their safety, is used because it is such a rich data source, with a history of forecasts, tide gauges and bathymetry measurements collected by video cameras. Even though the forecasts are on a local scale, the methods are generally applicable. It is shown that the intertidal beach volume change can be predicted up to three days ahead.Coastal EngineeringHydraulic Structures and Flood Ris

Real-time forecasting of morphological storm impacts: A case study in the Netherlands

Recent events like the Sumatra tsunami and Hurricane Katrina have reminded the world of the vulnerability of coastal areas to extreme events. Despite hydraulic engineering measures to minimize failure probability of coastal defence structures, a probability of failure, albeit small, remains. To assist local authorities and the population in their response to extreme events, timely access to relevant information of sufficient accuracy regarding impending natural threats is crucial. Current real-time systems do not include all relevant physics (e.g. morphodynamic response). This paper describes the efforts in the framework of the MICORE project to develop such an improved real-time system for the prediction of storm impacts. This paper addresses the proposed system architecture and some preliminary results. Also the paper addresses some aspects of the development environment that may be of more general interest than to this project alone.Civil Engineering and Geoscience

Lagrangian sediment transport modelling as a tool for investigating coastal connectivity

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.Environmental Fluid MechanicsGeo-engineeringCoastal Engineerin

MODELLING DUNE EROSION, OVERWASH AND BREACHING AT FIRE ISLAND (NY) DURING HURRICANE SANDY

In 2012, Hurricane Sandy caused a breach at Fire Island (NY, USA), near Pelican Island. This paper aims at modelling dune erosion, overwash and breaching processes that occured during the hurricane event at this stretch of coast with the numerical model XBeach. By using the default settings, the erosion rates are substantially overestimated, which was also concluded in several previous case studies. If the discretization of bed roughness along with wave skewness and asymmetry are improved in the model, XBeach is capable of simulating the various morphological changes within the chosen model domain

Lagrangian sediment transport modelling as a tool for investigating Coastal connectivity

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