CONTEXTField conditions and systems being assessed
Survey
Survey
Field data are essential in modeling because they provide information on the initial conditions and characteristics of the aquatic environment.
- 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
Survey
Clarity before a decision is made
Field data are essential in modeling because they provide information on the initial conditions and characteristics of the aquatic environment.
Survey activities are divided into three major groups: coastal surveys, marine surveys, and weather and climate surveys. Each group is classified based on the type of equipment used and the purpose of the survey.
Decision Supported
Define the approach, priorities, and actions for survey using traceable evidence.
Risk Controlled
Non-representative data, missed critical periods, and inefficient field cost.
Success Criteria
Quality-controlled data that meet the assessment objective and are analysis-ready.
Solution Paths
Choose the area that matches your need
7.01
Coastal
Modeling developed for coastal waters requires careful preparation of modeling scenarios, as well as the data and parameters needed as model inputs.
Learn more →
7.02
Marine
Modeling conducted in offshore waters over a relatively large area is generally easier to stabilize.
Learn more →
7.03
Weather and Climate
One of the most important factors in model development is the weather and climate parameters of a water area.
Learn more →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.
Field data are essential in modeling because they provide information on the initial conditions and characteristics of the aquatic environment. These data can be used to determine the initial conditions of the model to be developed. Boundary condition data for modeling can also be obtained from field measurements, allowing them to be compared with predicted boundary condition data. The most important role of field measurements is to support the verification and validation of model outputs.
Survey activities are divided into three major groups: coastal surveys, marine surveys, and weather and climate surveys. Each group is classified based on the type of equipment used and the purpose of the survey. Each type of equipment can measure one or more parameters and has its own technical specifications.
If survey activity support is needed for field data acquisition, please complete the form below. Survey equipment and technical personnel to operate the equipment in the field will be provided.
Setelah diklik untuk mengisi isiannya maka akan muncul form aplikasi sebagai berikut:
Related Options
Explore related topics and services
Home
CORZ turns data, numerical modeling, and multidisciplinary expertise into actionable recommendations for government, industry, researchers, and civil society organizations.
Learn more →
Model Applications
Indonesia, with one of the world's largest marine areas and longest coastlines, offers tremendous opportunities for offshore development and sustainable ocean-based industries.
Learn more →Modeling Modules
A wide range of numerical modeling modules can be applied to address coastal and marine challenges.
Learn more →Studies and Assessments
Various studies and assessments using modeling technology have been conducted in several Indonesian waters.
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