
Particle Tracking Module
The Particle Tracking Module is one of the most efficient tools for studying the distribution of dissolved and suspended particles in aquatic environments.
- 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
Particle Tracking Module
Clarity before a decision is made
The Particle Tracking Module is one of the most efficient tools for studying the distribution of dissolved and suspended particles in aquatic environments.
This module is highly effective for analyzing particle movement, transport pathways, and trajectories in water bodies. It can clearly simulate wind-driven particle transport and the influence of hydrodynamic processes on particle distribution.

Decision Supported
Define when and how to use particle tracking module, 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 particle tracking module.

Domain, grid, resolution, and time scale
This aspect is assessed to clarify its implications for particle tracking module.

Forcing, boundaries, and initial conditions
This aspect is assessed to clarify its implications for particle tracking module.

Parameterization, calibration, and validation
This aspect is assessed to clarify its implications for particle tracking module.

Scenarios, sensitivity, and uncertainty
This aspect is assessed to clarify its implications for particle tracking module.

Limitations and fitness for use
This aspect is assessed to clarify its implications for particle tracking module.
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 Particle Tracking Module is one of the most efficient tools for studying the distribution of dissolved and suspended particles in aquatic environments. The module uses a Lagrangian discretization technique, where the total particle mass in the system is divided into a number of particles with specific masses and positions in three-dimensional coordinates. Particle concentration within each grid cell or mesh element is then calculated using an Eulerian discretization method.
This module is highly effective for analyzing particle movement, transport pathways, and trajectories in water bodies. It can clearly simulate wind-driven particle transport and the influence of hydrodynamic processes on particle distribution. The main physical processes included in the model are dispersion, settling, buoyancy, and erosion. These processes are essential for understanding the behavior of particles with densities that differ from the surrounding water. A decay factor is also included for non-conservative substances, such as coliform bacteria and organic materials.
Horizontal eddy viscosity using the Smagorinsky formulation or constant eddy viscosity
Boundary conditions, including land boundaries, discharge, flux, or water level
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