
Elliptic Mild Slope Wave Model
The Elliptic Mild Slope Wave Model Module uses an efficient numerical solution of the mild-slope equation.
- Evidence-led
- Traceable assumptions
- Decision-ready outputs
- Methods proportionate to risk
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Clarity before a decision is made
Elliptic Mild Slope Wave Model
Clarity before a decision is made
The Elliptic Mild Slope Wave Model Module uses an efficient numerical solution of the mild-slope equation.
Partial wave refraction and transmission through piers, harbor structures, and breakwaters can also be represented. A sponge layer, or wave-absorbing layer, may be applied when a numerical solution requires wave energy absorption near the model boundaries.

Decision Supported
Define when and how to use elliptic mild slope 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.
What is assessed and why it matters

Represented physical or biogeochemical processes
This aspect is assessed to clarify its implications for elliptic mild slope wave model.

Domain, grid, resolution, and time scale
This aspect is assessed to clarify its implications for elliptic mild slope wave model.

Forcing, boundaries, and initial conditions
This aspect is assessed to clarify its implications for elliptic mild slope wave model.

Parameterization, calibration, and validation
This aspect is assessed to clarify its implications for elliptic mild slope wave model.

Scenarios, sensitivity, and uncertainty
This aspect is assessed to clarify its implications for elliptic mild slope wave model.

Limitations and fitness for use
This aspect is assessed to clarify its implications for elliptic mild slope wave model.
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.
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.
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.
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 Elliptic Mild Slope Wave Model Module uses an efficient numerical solution of the mild-slope equation. This equation is developed from the harmonic motion of infinitesimal-height waves over a seabed with a gradually varying slope. The module incorporates linear wave refraction–diffraction equations, including the effects of wave breaking, bottom friction, and wave damping.
Partial wave refraction and transmission through piers, harbor structures, and breakwaters can also be represented. A sponge layer, or wave-absorbing layer, may be applied when a numerical solution requires wave energy absorption near the model boundaries. The module also includes numerical formulations for wave radiation stresses, which are important for analyzing wave propagation across intersecting wave fields and in areas where strong wave diffraction occurs.
The Elliptic Mild Slope Wave Model Module uses a unique numerical solution method. Harmonic time variation is extracted, and the elliptic equation is formulated as mass and momentum equations. These equations are solved using a finite difference scheme with an Alternating Direction Implicit (ADI) algorithm. Wave-period calculations are derived from wave height, particle velocity components, sea level, and especially wave breaking, which is determined from wave radiation stresses within the modeled area.
This wave model is used to study wave resonance in harbors, long-period waves, and wave forces in relatively small coastal areas where wave diffraction and wave breaking are important. The dominant wave forcing is typically monochromatic and unidirectional. The module can be applied to all seabed depth profiles, although it has limitations in representing nonlinear effects, including wave-amplitude dispersion and wave-wave interactions. This model is particularly suitable for analyzing short-period wave disturbances inside harbors.
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.
- Location and project phase
- Decision or objective to support
- Primary problems and risks
- Available data
- Expected outputs and schedule