CONTEXTField conditions and systems being assessed
Training
Modeling
Many modeling software packages are currently available, both commercial and non-commercial.
- 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
Modeling
Clarity before a decision is made
Many modeling software packages are currently available, both commercial and non-commercial.
The advantage of non-commercial modeling software is that each line of the source code can be modified to improve the numerical logic used or to accommodate specific needs. One of its disadvantages is that it usually does not provide a user-friendly graphical interface, and changes made by users can be difficult to monitor.
Decision Supported
Define the approach, priorities, and actions for modeling using traceable evidence.
Risk Controlled
Generic content, poor fit with working tools, and limited practical transfer.
Success Criteria
Participants can apply methods to relevant data and cases.
Analysis Scope
What is assessed and why it matters
Participant profile and capability needs
This aspect is assessed to clarify its implications for modeling.
Concepts and working principles
This aspect is assessed to clarify its implications for modeling.
Exercises using data and cases
This aspect is assessed to clarify its implications for modeling.
Software and workflow
This aspect is assessed to clarify its implications for modeling.
Result interpretation and quality control
This aspect is assessed to clarify its implications for modeling.
Capability evaluation and follow-up
This aspect is assessed to clarify its implications for modeling.
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.
Many modeling software packages are currently available, both commercial and non-commercial. In general, non-commercial modeling software is developed as programs written in programming languages such as Fortran. Commercial modeling software, on the other hand, is usually compiled from programming languages such as C++ or Fortran and equipped with a Windows-based graphical user interface to make it easier for users to operate.
The advantage of non-commercial modeling software is that each line of the source code can be modified to improve the numerical logic used or to accommodate specific needs. One of its disadvantages is that it usually does not provide a user-friendly graphical interface, and changes made by users can be difficult to monitor.
The advantage of commercial modeling software is that it is generally easier to use because it provides a graphical user interface. The software is also usually more stable because it has gone through a strict internal debugging process. Its disadvantage is that users can only operate the software without being able to modify the numerical program behind it.
Many modeling software packages are now available, offering various model applications and model modules. Each software package has its own strengths and limitations, which are determined by many factors, such as the numerical equations used, model assumptions, model parameterization, and other related aspects. Because many types of modeling software are available, each with its own advantages and disadvantages, it can be difficult to determine which modeling software is most appropriate for a specific case in a particular aquatic environment.
Related Options
Explore related topics and services
Data Processing
Various types of data must be prepared as input data for modeling, and these data are obtained from many data providers, both national and international institutions.
Learn more →Data Interpretation
Unlike other types of training, modeling technology training does not focus only on technical skills, such as how to run or use available modeling software.
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