Development of a submerged aquatic vegetation growth model in the coupled ocean-atmosphere-wave-sediment transport (COAWST v3.4) model

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Kalra, T. S., Ganju, N. K., & Testa, J. M. Development of a submerged aquatic vegetation growth model in the coupled ocean-atmosphere-wave-sediment transport (COAWST v3.4) model. Geoscientific Model Development, 13(11), (2020): 5211-5228, doi:10.5194/gmd-13-5211-2020.The coupled biophysical interactions between submerged aquatic vegetation (SAV), hydrodynamics (currents and waves), sediment dynamics, and nutrient cycling have long been of interest in estuarine environments. Recent observational studies have addressed feedbacks between SAV meadows and their role in modifying current velocity, sedimentation, and nutrient cycling. To represent these dynamic processes in a numerical model, the presence of SAV and its effect on hydrodynamics (currents and waves) and sediment dynamics was incorporated into the open-source Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model. In this study, we extend the COAWST modeling framework to account for dynamic changes of SAV and associated epiphyte biomass. Modeled SAV biomass is represented as a function of temperature, light, and nutrient availability. The modeled SAV community exchanges nutrients, detritus, dissolved inorganic carbon, and dissolved oxygen with the water-column biogeochemistry model. The dynamic simulation of SAV biomass allows the plants to both respond to and cause changes in the water column and sediment bed properties, hydrodynamics, and sediment transport (i.e., a two-way feedback). We demonstrate the behavior of these modeled processes through application to an idealized domain and then apply the model to a eutrophic harbor where SAV dieback is a result of anthropogenic nitrate loading and eutrophication. These cases demonstrate an advance in the deterministic modeling of coupled biophysical processes and will further our understanding of future ecosystem change.This is University of Maryland Center for Environmental Contribution no. 5909

Development of a coupled wave-flow-vegetation interaction model

© The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Computers & Geosciences 100 (2017): 76–86, doi:10.1016/j.cageo.2016.12.010.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.This study was part of the Estuarine Physical Response to Storms project (GS2-2D), supported by the Department of Interior Hurricane Sandy Recovery program

Using tracer variance decay to quantify variability of salinity mixing in the Hudson River Estuary

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Warner, J. C., Geyer, W. R., Ralston, D. K., & Kalra, T. Using tracer variance decay to quantify variability of salinity mixing in the Hudson River Estuary. Journal of Geophysical Research: Oceans, 125(12), (2020): e2020JC016096, https://doi.org/10.1029/2020JC016096.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.This study was funded through the Coastal Model Applications and Field Measurements Project and the Cross‐shore and Inlets Project, US Geological Survey Coastal Marine Hazards and Resources Program. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government

A geospatially resolved wetland vulnerability index: synthesis of physical drivers

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Defne, Z., Aretxabaleta, A. L., Ganju, N. K., Kalra, T. S., Jones, D. K., & Smith, K. E. L. A geospatially resolved wetland vulnerability index: synthesis of physical drivers. Plos One, 15(1), (2020): e0228504, doi:10.1371/journal.pone.0228504.Assessing wetland vulnerability to chronic and episodic physical drivers is fundamental for establishing restoration priorities. We synthesized multiple data sets from E.B. Forsythe National Wildlife Refuge, New Jersey, to establish a wetland vulnerability metric that integrates a range of physical processes, anthropogenic impact and physical/biophysical features. The geospatial data are based on aerial imagery, remote sensing, regulatory information, and hydrodynamic modeling; and include elevation, tidal range, unvegetated to vegetated marsh ratio (UVVR), shoreline erosion, potential exposure to contaminants, residence time, marsh condition change, change in salinity, salinity exposure and sediment concentration. First, we delineated the wetland complex into individual marsh units based on surface contours, and then defined a wetland vulnerability index that combined contributions from all parameters. We applied principal component and cluster analyses to explore the interrelations between the data layers, and separate regions that exhibited common characteristics. Our analysis shows that the spatial variation of vulnerability in this domain cannot be explained satisfactorily by a smaller subset of the variables. The most influential factor on the vulnerability index was the combined effect of elevation, tide range, residence time, and UVVR. Tide range and residence time had the highest correlation, and similar bay-wide spatial variation. Some variables (e.g., shoreline erosion) had no significant correlation with the rest of the variables. The aggregated index based on the complete dataset allows us to assess the overall state of a given marsh unit and quickly locate the most vulnerable units in a larger marsh complex. The application of geospatially complete datasets and consideration of chronic and episodic physical drivers represents an advance over traditional point-based methods for wetland assessment.This study was part of the Estuarine Physical Response to Storms project (GS2-2D awarded to NKG), supported by the Department of the Interior Hurricane Sandy Recovery program. Support was also provided by the U.S. Geological Survey, Coastal and Marine Hazards/Resources Program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Modeling the morphodynamics of coastal responses to extreme events: what shape are we in?

This paper is not subject to U.S. copyright. The definitive version was published in Sherwood, C. R., van Dongeren, A., Doyle, J., Hegermiller, C. A., Hsu, T.-J., Kalra, T. S., Olabarrieta, M., Penko, A. M., Rafati, Y., Roelvink, D., van der Lugt, M., Veeramony, J., & Warner, J. C. Modeling the morphodynamics of coastal responses to extreme events: what shape are we in? Annual Review of Marine Science, 14, (2022): 457–492, https://doi.org/10.1146/annurev-marine-032221-090215.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.All authors except D.R. were partially supported by the IFMSIP project, funded by US Office of Naval Research grant PE 0601153N under contracts N00014-17-1-2459 (Deltares), N00014-18-1-2785 (University of Delaware), N0001419WX00733 (US Naval Research Laboratory, Monterey), N0001418WX01447 (US Naval Research Laboratory, Stennis Space Center), and N0001418IP00016 (US Geological Survey). C.R.S., C.A.H., T.S.K., and J.C.W. were supported by the US Geological Survey Coastal/Marine Hazards and Resources Program. A.v.D. and M.v.d.L. were supported by the Deltares Strategic Research project Quantifying Flood Hazards and Impacts. M.O. acknowledges support from National Science Foundation project OCE-1554892

Regime Changes in Global Sea Surface Salinity Trend

17 pages, 9 figuresRecent studies have shown significant sea surface salinity (SSS) changes at scales ranging from regional to global. In this study, we estimate global salinity means and trends using historical (1950–2014) SSS data from the UK Met Office Hadley Centre objectively analyzed monthly fields and recent data from the SMOS satellite (2010–2014). We separate the different components (regimes) of the global surface salinity by fitting a Gaussian Mixture Model to the data and using expectation–maximization to distinguish the means and trends of the data. The procedure uses a non-subjective method (Bayesian information criterion) to extract the optimal number of means and trends. The results show the presence of three separate regimes: Regime A (1950–1990) is characterized by small trend magnitudes; Regime B (1990–2009) exhibited enhanced trends; and Regime C (2009–2014) with significantly larger trend magnitudes. The salinity differences between regime means were around 0.01. The trend acceleration could be related to an enhanced global hydrological cycle or to a change in the sampling methodology. Understanding past SSS changes can provide insight into future climate evolution by complementing the knowledge acquired in recent decades from long-term temperature record analysesThis study has been funded by the Spanish Ministry of Economy through the National R1D Plan by means of MIDAS-7 Project AYA2012˘39356−C05−03. [...] The SMOS data were produced by the Barcelona Expert Centre (www.smos-bec.icm.csic.es), a joint initiative of the Spanish Research Council (CSIC) and the Technical University of Catalonia (UPC), mainly funded by the Spanish National Program on Space. [...] A.L. Aretxabaleta was supported by a Juan de la Cierva grant from the Spanish Government during the early stages of the study. K.W. Smith was supported by his wages at Lowe Energy DesignPeer Reviewe

Modeling the dynamics of salt marsh development in coastal land reclamation

Author Posting. © American Geophysical Union, 2022. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 49(6), (2022): e2021GL095559, https://doi.org/10.1029/2021GL095559.The valuable ecosystem services of salt marshes are spurring marsh restoration projects around the world. However, it is difficult to determine the final vegetated area based on physical drivers. Herein, we use a 3D fully coupled vegetation-hydrodynamic-morphological modeling system to simulate the final vegetation cover and the timescale to reach it under various forcing conditions. Marsh development in our simulations can be divided in three distinctive phases: A preparation phase characterized by sediment accumulation in the absence of vegetation, an encroachment phase in which the vegetated area grows, and an adjustment phase in which the vegetated area remains relatively constant while marsh accretes vertically to compensate for sea level rise. Sediment concentration, settling velocity, sea level rise, and tidal range each comparably affect equilibrium coverage and timescale in different ways. Our simulations show that the Unvegetated-Vegetated Ratio also relates to sediment budget in marsh development under most conditions.This study was supported by the Department of the Interior Hurricane Sandy Recovery program (ID G16AC00455), NSF awards 1637630 (PIE LTER) and 1832221 (VCR LTER), and China Scholarship Council.2022-09-1

Comparison of physical to numerical mixing with different tracer advection schemes in estuarine environments

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Kalra, T. S., Li, X., Warner, J. C., Geyer, W. R., & Wu, H. Comparison of physical to numerical mixing with different tracer advection schemes in estuarine environments. Journal of Marine Science and Engineering, 7(10), (2019): 338, doi: 10.3390/jmse7100338.The 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.This study was funded through the Coastal Model Applications and Field Measurements Project and the Cross-shore and Inlets Project, US Geological Survey Coastal Marine Hazards and Resources Program

Modeling of barrier breaching during hurricanes Sandy and Matthew

This paper is not subject to U.S. copyright. The definitive version was published in Hegermiller, C. A., Warner, J. C., Olabarrieta, M., Sherwood, C. R., & Kalra, T. S. Modeling of barrier breaching during hurricanes Sandy and Matthew. Journal of Geophysical Research: Earth Surface, 127(3), (2022): e2021JF006307, https://doi.org/10.1029/2021JF006307.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.C. A. Hegermiller is grateful to the U.S. Geological Survey (USGS) Mendenhall Research Fellowship Program for support. This project was supported by the USGS Coastal and Marine Geology Program and the Office of Naval Research, Increasing the Fidelity of Morphological Storm Impact Predictions Project. M. Olabarrieta acknowledges support from the NSF project OCE-1554892.2022-07-2