
Wave Refraction–Diffraction
The Wave Refraction–Diffraction Module is an integrated wave modeling framework that combines several wave modules, including the Spectral Wave Model, the Shallow-Water Spectral Wave Model, the Parabolic…
- Evidence-led
- Traceable assumptions
- Decision-ready outputs
- Methods proportionate to risk
One-Page Visual Summary for Quick Briefing
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Clarity before a decision is made
Wave Refraction–Diffraction
Clarity before a decision is made
The Wave Refraction–Diffraction Module is an integrated wave modeling framework that combines several wave modules, including the Spectral Wave Model, the Shallow-Water Spectral Wave Model, the Parabolic…
This module is designed to support detailed analysis of the key factors that control wave refraction and diffraction. Wave-generating forces may vary, and each simulation can apply different assumptions and mathematical formulations depending on the modeling objective, domain scale, and wave conditions.

Decision Supported
Define when and how to use wave refraction–diffraction, 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 wave refraction–diffraction.

Domain, grid, resolution, and time scale
This aspect is assessed to clarify its implications for wave refraction–diffraction.

Forcing, boundaries, and initial conditions
This aspect is assessed to clarify its implications for wave refraction–diffraction.

Parameterization, calibration, and validation
This aspect is assessed to clarify its implications for wave refraction–diffraction.

Scenarios, sensitivity, and uncertainty
This aspect is assessed to clarify its implications for wave refraction–diffraction.

Limitations and fitness for use
This aspect is assessed to clarify its implications for wave refraction–diffraction.
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 Wave Refraction–Diffraction Module is an integrated wave modeling framework that combines several wave modules, including the Spectral Wave Model, the Shallow-Water Spectral Wave Model, the Parabolic Mild-Slope Wave Model for large-area applications, and the Elliptic Mild-Slope Wave Model for smaller domains such as harbors.
This module is designed to support detailed analysis of the key factors that control wave refraction and diffraction. Wave-generating forces may vary, and each simulation can apply different assumptions and mathematical formulations depending on the modeling objective, domain scale, and wave conditions.
The combination of wave forcing, selected assumptions, and appropriate formulations enables users to analyze wave refraction and diffraction patterns, as well as their propagation behavior. The simulation is useful for developing suitable scenarios to represent short-period and long-period wave transformation. The resulting wave refraction and diffraction conditions depend on the size of the model domain and the mathematical formulation used.
The required input data depend on the selected wave-generating forces and modeling assumptions. More detailed input requirements can be referred to in the related modules, including the Spectral Wave Model, Shallow-Water Spectral Wave Model, Parabolic Mild-Slope Wave Model for large domains, and Elliptic Mild-Slope Wave Model for limited-area domains such as 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