Development of a submerged aquatic vegetation growth model in the Coupled Ocean–Atmosphere–Wave–Sediment Transport  (COAWST v3.4) model

Abstract. 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.

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Development of a Submerged Aquatic Vegetation Growth Model in a Coupled Wave-Current-Sediment-Transport Modeling System (COAWST v3.4)

10.5194/gmd-2018-271 ◽

2019 ◽

Author(s):

Tarandeep S. Kalra ◽

Neil K. Ganju ◽

Jeremy M. Testa

Keyword(s):

Sediment Transport ◽

Nutrient Cycling ◽

Water Column ◽

Submerged Aquatic Vegetation ◽

Aquatic Vegetation ◽

Dissolved Inorganic Carbon ◽

Sediment Dynamics ◽

Velocity Sedimentation ◽

Ecosystem Change ◽

Deterministic Modelling

Abstract. 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, current velocity, sedimentation, and nutrient cycling and suggest SAV are ecosystem engineers whose growth can be self-reinforcing. 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 model COAWST. In this study, we extend the COAWST modelling framework to account for dynamic changes of SAV and associated epiphyte biomass. Modelled SAV biomass is represented as a function of temperature, light, and nutrient availability and 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 water column and sediment bed properties, hydrodynamics, and sediment transport (i.e., a two-way feedback). We demonstrate the behavior of these modelled processes through application to an idealized domain, then apply the model to a eutrophic harbour where SAV dieback is a result of anthropogenic nitrate loading and eutrophication. These cases demonstrate an advance in the deterministic modelling of coupled bio-physical processes and will further our understanding of future ecosystem change.

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Influence of invasive submerged aquatic vegetation ( E. densa ) on currents and sediment transport in a freshwater tidal system

Water Resources Research ◽

10.1029/2020wr028789 ◽

2021 ◽

Author(s):

Jessica R. Lacy ◽

Madeline R. Foster‐Martinez ◽

Rachel M. Allen ◽

Judith Z. Drexler

Keyword(s):

Sediment Transport ◽

Submerged Aquatic Vegetation ◽

Aquatic Vegetation

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Development of the CSOMIO Coupled Ocean-Oil-Sediment- Biology Model

Frontiers in Marine Science ◽

10.3389/fmars.2021.629299 ◽

2021 ◽

Vol 8 ◽

Author(s):

Dmitry S. Dukhovskoy ◽

Steven L. Morey ◽

Eric P. Chassignet ◽

Xu Chen ◽

Victoria J. Coles ◽

...

Keyword(s):

Sediment Transport ◽

Water Column ◽

Microbial Interactions ◽

Ocean Dynamics ◽

Regional Ocean Modeling System ◽

Coupled Modeling ◽

Modeling Framework ◽

Oil Droplets ◽

Plume Model ◽

Oil Components

The fate and dispersal of oil in the ocean is dependent upon ocean dynamics, as well as transformations resulting from the interaction with the microbial community and suspended particles. These interaction processes are parameterized in many models limiting their ability to accurately simulate the fate and dispersal of oil for subsurface oil spill events. This paper presents a coupled ocean-oil-biology-sediment modeling system developed by the Consortium for Simulation of Oil-Microbial Interactions in the Ocean (CSOMIO) project. A key objective of the CSOMIO project was to develop and evaluate a modeling framework for simulating oil in the marine environment, including its interaction with microbial food webs and sediments. The modeling system developed is based on the Coupled Ocean-Atmosphere-Wave-Sediment Transport model (COAWST). Central to CSOMIO’s coupled modeling system is an oil plume model coupled to the hydrodynamic model (Regional Ocean Modeling System, ROMS). The oil plume model is based on a Lagrangian approach that describes the oil plume dynamics including advection and diffusion of individual Lagrangian elements, each representing a cluster of oil droplets. The chemical composition of oil is described in terms of three classes of compounds: saturates, aromatics, and heavy oil (resins and asphaltenes). The oil plume model simulates the rise of oil droplets based on ambient ocean flow and density fields, as well as the density and size of the oil droplets. The oil model also includes surface evaporation and surface wind drift. A novel component of the CSOMIO model is two-way Lagrangian-Eulerian mapping of the oil characteristics. This mapping is necessary for implementing interactions between the ocean-oil module and the Eulerian sediment and biogeochemical modules. The sediment module is a modification of the Community Sediment Transport Modeling System. The module simulates formation of oil-particle aggregates in the water column. The biogeochemical module simulates microbial communities adapted to the local environment and to elevated concentrations of oil components in the water column. The sediment and biogeochemical modules both reduce water column oil components. This paper provides an overview of the CSOMIO coupled modeling system components and demonstrates the capabilities of the modeling system in the test experiments.

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A Review of Remote Sensing of Submerged Aquatic Vegetation for Non-Specialists

Remote Sensing ◽

10.3390/rs13040623 ◽

2021 ◽

Vol 13 (4) ◽

pp. 623

Author(s):

Gillian S. L. Rowan ◽

Margaret Kalacska

Keyword(s):

Remote Sensing ◽

Water Column ◽

Large Scale ◽

Submerged Aquatic Vegetation ◽

Aquatic Vegetation ◽

Global Climate ◽

Training Data ◽

Suspended Particulate ◽

The Subject

Submerged aquatic vegetation (SAV) is a critical component of aquatic ecosystems. It is however understudied and rapidly changing due to global climate change and anthropogenic disturbances. Remote sensing (RS) can provide the efficient, accurate and large-scale monitoring needed for proper SAV management and has been shown to produce accurate results when properly implemented. Our objective is to introduce RS to researchers in the field of aquatic ecology. Applying RS to underwater ecosystems is complicated by the water column as water, and dissolved or suspended particulate matter, interacts with the same energy that is reflected or emitted by the target. This is addressed using theoretical or empiric models to remove the water column effect, though no model is appropriate for all aquatic conditions. The suitability of various sensors and platforms to aquatic research is discussed in relation to both SAV as the subject and to project aims and resources. An overview of the required corrections, processing and analysis methods for passive optical imagery is presented and discussed. Previous applications of remote sensing to identify and detect SAV are briefly presented and notable results and lessons are discussed. The success of previous work generally depended on the variability in, and suitability of, the available training data, the data’s spatial and spectral resolutions, the quality of the water column corrections and the level to which the SAV was being investigated (i.e., community versus species.)

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