CONTEXTField conditions and systems being assessed
Modeling Modules
Boussinesq Wave Model
The Boussinesq Wave Model Module is an advanced wave modeling module that uses complex numerical formulations.
- Evidence-led
- Traceable assumptions
- Decision-ready outputs
- Methods proportionate to risk
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ANALYSISIntegrated data, methods, and modelling
DECISIONVisual outputs and actionable recommendations
Executive Brief
Clarity before a decision is made
01Evidence-led
02Traceable assumptions
03Decision-ready outputs
04Methods proportionate to risk
Executive Brief
Boussinesq Wave Model
Clarity before a decision is made
The Boussinesq Wave Model Module is an advanced wave modeling module that uses complex numerical formulations.
The Boussinesq Wave Model solves the governing equations using a flux-formulation approach, while adding the linear dispersion characteristics of waves. This formulation was first developed by Madsen et al. (1991) and Madsen and Sørensen (1992), enabling the successful simulation of Boussinesq wave propagation from deep water to shallow water.
Decision Supported
Define when and how to use boussinesq wave model, including required data, configuration, validation, and scenarios.
Risk Controlled
Non-representative models, insufficient data, weak validation, and over-interpretation.
Success Criteria
Transparent, validated models that respond to scenarios at the decision scale.
Analysis Scope
What is assessed and why it matters
Represented physical or biogeochemical processes
This aspect is assessed to clarify its implications for boussinesq wave model.
Domain, grid, resolution, and time scale
This aspect is assessed to clarify its implications for boussinesq wave model.
Forcing, boundaries, and initial conditions
This aspect is assessed to clarify its implications for boussinesq wave model.
Parameterization, calibration, and validation
This aspect is assessed to clarify its implications for boussinesq wave model.
Scenarios, sensitivity, and uncertainty
This aspect is assessed to clarify its implications for boussinesq wave model.
Limitations and fitness for use
This aspect is assessed to clarify its implications for boussinesq wave model.
Data & Methods
A traceable evidence base
Observations
Field surveys, in-situ measurements, laboratory results, historical records, and operating information as required.
Remote sensing & GIS
Satellite imagery, mapping, spatial analysis, temporal change, and integration of multiple data sources.
Modeling & scenarios
Model setup, calibration, validation, existing–planned–extreme scenarios, and sensitivity analysis.
Quality assurance
Metadata, quality controls, assumptions, limitations, data versions, and processing lineage are documented.
Core Deliverables
Decision-ready information
Initial assessment & data gaps
Objectives, study area, available data, additional needs, initial risks, and recommended level of detail.
Datasets, maps & indicators
Quality-controlled data, thematic maps, time series, indicators, and comparable visualizations.
Scenarios & risk evaluation
Comparison of existing conditions, alternatives, extremes, sensitivities, consequences, and mitigation options.
Report & executive brief
Methods, results, limitations, recommendations, action priorities, and stakeholder presentation materials.
Decision Value
Benefits for decision makers and policy leaders
Reduce uncertainty
Assumptions, data, variability, and limitations are stated so decision risk is not hidden.
Compare options objectively
Alternative locations, designs, operations, or policies are assessed using consistent indicators.
Optimize cost and time
Data needs and analysis depth are proportionate to risk so resources are used efficiently.
Increase stakeholder confidence
Findings and recommendations are transparent for technical, management, regulatory, and partner review.
Delivery Path
A clear process from need to recommendation
- 01
Need definition
Objectives, users, location, project phase, problems, constraints, and the decision to support.
- 02
Scope & work plan
Methods, data, surveys, models, schedule, team, deliverables, review gates, and resource estimate.
- 03
Acquisition & quality control
Collection, inspection, harmonization, documentation, and data-sufficiency assessment.
- 04
Analysis & scenario testing
Processing, modeling, validation, option comparison, sensitivity, and risk evaluation.
- 05
Recommendation & handover
Maps, report, executive brief, presentation, supporting data, and follow-up plan.
Full technical basis and contextOpen this section to read the complete source technical narrative.
The Boussinesq Wave Model Module is an advanced wave modeling module that uses complex numerical formulations. The Boussinesq equations include the effects of nonlinearity and frequency dispersion. In principle, frequency dispersion is incorporated into the momentum equations to represent the effect of vertical acceleration on pressure variations within the water column.
The Boussinesq Wave Model solves the governing equations using a flux-formulation approach, while adding the linear dispersion characteristics of waves. This formulation was first developed by Madsen et al. (1991) and Madsen and Sørensen (1992), enabling the successful simulation of Boussinesq wave propagation from deep water to shallow water.
The Boussinesq Wave Model was later further developed for the surf zone by incorporating formulations for wave breaking and shoreline wave motion, as described by Madsen et al. (1997a, b), Sørensen and Sørensen (2001), and Sørensen et al. (1998, 2004).
This module is capable of simulating the combined effects of wave processes in marinas, harbors, and coastal areas, including:
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Learn more →Next Step
Share the need, location, available data, and the decision to be supported.
The CORZ team will review the objective, scope, data availability, risk level, schedule, and required outputs to prepare a proportionate approach.
Useful initial information
- Location and project phase
- Decision or objective to support
- Primary problems and risks
- Available data
- Expected outputs and schedule