Warner
John C.
Warner
John C.
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ArticleModeling the morphodynamics of coastal responses to extreme events: what shape are we in?(Annual Reviews, 2021-07-27) Sherwood, Christopher R. ; van Dongeren, Ap ; Doyle, James D. ; Hegermiller, Christie A. ; Hsu, Tian-Jian ; Kalra, Tarandeep S. ; Olabarrieta, Maitane ; Penko, Allison M. ; Rafati, Yashar ; Roelvink, Dano ; van der Lugt, Marlies ; Veeramony, Jay ; Warner, John C.This review focuses on recent advances in process-based numerical models of the impact of extreme storms on sandy coasts. Driven by larger-scale models of meteorology and hydrodynamics, these models simulate morphodynamics across the Sallenger storm-impact scale, including swash,collision, overwash, and inundation. Models are becoming both wider (as more processes are added) and deeper (as detailed physics replaces earlier parameterizations). Algorithms for wave-induced flows and sediment transport under shoaling waves are among the recent developments. Community and open-source models have become the norm. Observations of initial conditions (topography, land cover, and sediment characteristics) have become more detailed, and improvements in tropical cyclone and wave models provide forcing (winds, waves, surge, and upland flow) that is better resolved and more accurate, yielding commensurate improvements in model skill. We foresee that future storm-impact models will increasingly resolve individual waves, apply data assimilation, and be used in ensemble modeling modes to predict uncertainties.
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ArticleModeling of barrier breaching during hurricanes Sandy and Matthew(American Geophysical Union, 2022-01-26) Hegermiller, Christie A. ; Warner, John C. ; Olabarrieta, Maitane ; Sherwood, Christopher R. ; Kalra, Tarandeep S.Physical processes driving barrier island change during storms are important to understand to mitigate coastal hazards and to evaluate conceptual models for barrier evolution. Spatial variations in barrier island topography, landcover characteristics, and nearshore and back-barrier hydrodynamics can yield complex morphological change that requires models of increasing resolution and physical complexity to predict. Using the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) modeling system, we investigated two barrier island breaches that occurred on Fire Island, NY during Hurricane Sandy (2012) and at Matanzas, FL during Hurricane Matthew (2016). The model employed a recently implemented infragravity (IG) wave driver to represent the important effects of IG waves on nearshore water levels and sediment transport. The model simulated breaching and other changes with good skill at both locations, resolving differences in the processes and evolution. The breach simulated at Fire Island was 250 m west of the observed breach, whereas the breach simulated at Matanzas was within 100 m of the observed breach. Implementation of the vegetation module of COAWST to allow three-dimensional drag over dune vegetation at Fire Island improved model skill by decreasing flows across the back-barrier, as opposed to varying bottom roughness that did not positively alter model response. Analysis of breach processes at Matanzas indicated that both far-field and local hydrodynamics influenced breach creation and evolution, including remotely generated waves and surge, but also surge propagation through back-barrier waterways. This work underscores the importance of resolving the complexity of nearshore and back-barrier systems when predicting barrier island change during extreme events.
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ArticleShoaling wave shape estimates from field observations and derived bedload sediment rates(MDPI, 2022-02-08) Kalra, Tarandeep S. ; Suttles, Steven E. ; Sherwood, Christopher R. ; Warner, John C. ; Aretxabaleta, Alfredo L. ; Leavitt, Gibson R.he shoaling transformation from generally linear deep-water waves to asymmetric shallow-water waves modifies wave shapes and causes near-bed orbital velocities to become asymmetrical, contributing to net sediment transport. In this work, we used two methods to estimate the asymmetric wave shape from data at three sites. The first method converted wave measurements made at the surface to idealized near-bottom wave-orbital velocities using a set of empirical equations: the “parameterized” waveforms. The second method involved direct measurements of velocities and pressure made near the seabed: the “direct” waveforms. Estimates from the two methods were well correlated at all three sites (Pearson’s correlation coefficient greater than 0.85). Both methods were used to drive bedload-transport calculations that accounted for asymmetric waves, and the results were compared with a traditional excess-stress formulation and field estimates of bedload transport derived from ripple migration rates based on sonar imagery. The cumulative bedload transport from the parameterized waveform was 25% greater than the direct waveform, mainly because the parameterized waveform did not account for negative skewness. Calculated transport rates were comparable to rates estimated from ripple migration except during the largest event, when calculated rates were as much as 100 times greater, which occurred during high period waves.
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ArticleDevelopment of a coupled wave-flow-vegetation interaction model(Elsevier, 2016-12-15) Beudin, Alexis ; Kalra, Tarandeep S. ; Ganju, Neil K. ; Warner, John C.Emergent and submerged vegetation can significantly affect coastal hydrodynamics. However, most deterministic numerical models do not take into account their influence on currents, waves, and turbulence. In this paper, we describe the implementation of a wave-flow-vegetation module into a Coupled-Ocean-Atmosphere-Wave-Sediment Transport (COAWST) modeling system that includes a flow model (ROMS) and a wave model (SWAN), and illustrate various interacting processes using an idealized shallow basin application. The flow model has been modified to include plant posture-dependent three-dimensional drag, in-canopy wave-induced streaming, and production of turbulent kinetic energy and enstrophy to parameterize vertical mixing. The coupling framework has been updated to exchange vegetation-related variables between the flow model and the wave model to account for wave energy dissipation due to vegetation. This study i) demonstrates the validity of the plant posture-dependent drag parameterization against field measurements, ii) shows that the model is capable of reproducing the mean and turbulent flow field in the presence of vegetation as compared to various laboratory experiments, iii) provides insight into the flow-vegetation interaction through an analysis of the terms in the momentum balance, iv) describes the influence of a submerged vegetation patch on tidal currents and waves separately and combined, and v) proposes future directions for research and development.
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ArticleUsing tracer variance decay to quantify variability of salinity mixing in the Hudson River Estuary(American Geophysical Union, 2020-11-12) Warner, John C. ; Geyer, W. Rockwell ; Ralston, David K. ; Kalra, Tarandeep S.The salinity structure in an estuary is controlled by time‐dependent mixing processes. However, the locations and temporal variability of where significant mixing occurs is not well‐understood. Here we utilize a tracer variance approach to demonstrate the spatial and temporal structure of salinity mixing in the Hudson River Estuary. We run a 4‐month hydrodynamic simulation of the tides, currents, and salinity that captures the spring‐neap tidal variability as well as wind‐driven and freshwater flow events. On a spring‐neap time scale, salinity variance dissipation (mixing) occurs predominantly during the transition from neap to spring tides. On a tidal time scale, 60% of the salinity variance dissipation occurs during ebb tides and 40% during flood tides. Spatially, mixing during ebbs occurs primarily where lateral bottom salinity fronts intersect the bed at the transition from the main channel to adjacent shoals. During ebbs, these lateral fronts form seaward of constrictions located at multiple locations along the estuary. During floods, mixing is generated by a shear layer elevated in the water column at the top of the mixed bottom boundary layer, where variations in the along channel density gradients locally enhance the baroclinic pressure gradient leading to stronger vertical shear and more mixing. For both ebb and flood, the mixing occurs at the location of overlap of strong vertical stratification and eddy diffusivity, not at the maximum of either of those quantities. This understanding lends a new insight to the spatial and time dependence of the estuarine salinity structure.
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ArticleComparison of physical to numerical mixing with different tracer advection schemes in estuarine environments(MDPI, 2019-09-26) Kalra, Tarandeep S. ; Li, Xiangyu ; Warner, John C. ; Geyer, W. Rockwell ; Wu, HuiThe numerical simulation of estuarine dynamics requires accurate prediction for the transport of tracers, such as temperature and salinity. During the simulation of these processes, all the numerical models introduce two kinds of tracer mixing: (1) by parameterizing the tracer eddy diffusivity through turbulence models leading to a source of physical mixing and (2) discretization of the tracer advection term that leads to numerical mixing. Physical and numerical mixing both vary with the choice of horizontal advection schemes, grid resolution, and time step. By simulating four idealized cases, this study compares the physical and numerical mixing for three different tracer advection schemes. Idealized domains only involving physical and numerical mixing are used to verify the implementation of mixing terms by equating them to total tracer variance. Among the three horizontal advection schemes, the scheme that causes the least numerical mixing while maintaining a sharp front also results in larger physical mixing. Instantaneous spatial comparison of mixing components shows that physical mixing is dominant in regions of large vertical gradients, while numerical mixing dominates at sharp fronts that contain large horizontal tracer gradients. In the case of estuaries, numerical mixing might locally dominate over physical mixing; however, the amount of volume integrated numerical mixing through the domain compared to integrated physical mixing remains relatively small for this particular modeling system.