Strategies for transforming fine scale knowledge to management usability

Area of Science:

• Environmental modeling
• Computational science
• Ecosystem management

Background:

• Management tools require computationally fast, user-friendly, realistic, generic, and reliable simulations.
• Model complexity and simplicity are often conflicting requirements in environmental modeling.

Purpose of the Study:

• To develop a strategy for overcoming the conflicting demands of complexity and simplicity in environmental modeling for management.
• To introduce model analysis and reduction as core components of the environmental modeling process.

Main Methods:

• A set of combined methods for automatic exploration of model behavior.
• Techniques for aggregating fine-scale process knowledge with spatio-temporal resolution.
• Application of methods to European Regional Seas Ecosystem Model (ERSEM), Photosynthesis model (PGEN), and a diagenetic model.

Main Results:

• Demonstrated successful application of analysis and aggregation methods to diverse ecosystem and process models.
• Showcased the ability to explore model behavior and integrate detailed process knowledge.
• Validated the approach on complex models like ERSEM and PGEN.

Conclusions:

• The proposed analysis and aggregation methods represent a significant step towards next-generation decision support tools.
• These tools can effectively handle increasing scientific knowledge and evolving management demands.
• The strategy provides a pathway to create more effective and adaptable environmental management simulations.