Model Library

Capabilities

• SHOC is a general-purpose model for simulating estuaries to regional ocean domains, supporting particle tracking and coupling with ecological and sediment transport models (Blumberg and Herring, 1987; CSIRO, 2022);
• It employs a sparse coordinate system for efficient domain decomposition, distributed processing, resolution optimization, and reduced computational demands (CSIRO, 2022; Herzfeld, 2006; Langlais et al., 2021);
• SHOC supports automated model setup, where the model parameters are estimated from grid and bathymetric information (CSIRO, 2022);
• The model supports complex curvilinear grids, 2-way nesting, and hybrid physics, enhancing computational flexibility and speed (Langlais et al., 2021);
• SHOC uses a free surface with mode splitting to distinguish 2D and 3D processes for detailed simulations (Langlais et al., 2021);
• It enables the simulation of sediment, nutrient, and pesticide transport and transformation, forming a foundation to build other components of future receiving water quality models for the Great Barrier Reef (Brinkman et al., 2011; Edmunds, 2011).

 Limitations

• It is not a public domain model (Andréfouët et al., 2006);
• It requires large computer memory for analysis due to high resolution (Edmunds, 2011);
• It is highly complex (Edmunds, 2011);
• It only runs under the Linux operating system (Chen et al., 2011).

Inputs

Grid and bathymetry data, Fresh water flows from rivers, Meteorological conditions across the surface, Surface elevation, Water quality data.

Outputs

Outputs from the model include 3D distributions of velocity, temperature, salinity, density, passive tracers, mixing coefficients, sea level, particle tracking, simulations of the transport and mixing of water, heat, salt, substances from sediment and biogeochemical models, and diagnostics, including momentum balance terms, vorticity, steric height, stability constraints, flushing times, mixed layer depth, turbulent mixing lengths, mean currents, and tracer fluxes.

References

Williams, R. N., de Souza, P. A., & Jones, E. M. (2014). Analysing coastal ocean model outputs using competitive-learning pattern recognition techniques. Environmental Modelling & Software, 57, 165–176. https://doi.org/10.1016/j.envsoft.2014.03.001

Baird, M. E., Mongin, M., Skerratt, J., Margvelashvili, N., Tickell, S., Steven, A. D. L., Robillot, C., Ellis, R., Waters, D., Kaniewska, P., & Brodie, J. (2021). Impact of catchment-derived nutrients and sediments on marine water quality on the Great Barrier Reef: An application of the eReefs marine modelling system. Marine Pollution Bulletin, 167, 112297. https://doi.org/10.1016/j.marpolbul.2021.112297

Objectives

The overall goal of this study was to demonstrate the use of competitive-learning pattern recognition techniques to interpret the dynamics in a coastal system where we presently have a good understanding of the different environmental conditions and their drivers.

The objective of the study was to quantify the impact of anthropogenic catchment loads of sediments and nutrients on marine water quality variables in the Great Barrier Reef, demonstrate the benefits of improved catchment management, and determine basin-specific load reduction targets to minimize ecological impacts. The study also aims to advance previous research by analyzing new catchment load scenarios and distinguishing between natural and anthropogenic impacts.