Towards a comprehensive uncertainty assessment in environmental research and decision support

Area of Science:

• Environmental Science
• Decision Science
• Statistical Modeling

Background:

• Uncertainty quantification is crucial for environmental management, yet often incomplete in decision support systems.
• The robustness of uncertainty quantification assumptions is frequently overlooked in environmental research.
• Existing methods often fail to fully incorporate uncertainties related to predictions and societal preferences.

Purpose of the Study:

• To demonstrate a comprehensive approach to uncertainty quantification in environmental research and decision support.
• To integrate established and novel statistical techniques for more robust environmental management.
• To address previously ignored uncertainties in predictions and preferences within decision-making frameworks.

Main Methods:

• Utilizing stochastic, mechanistic models to propagate uncertainties from source to output.
• Employing data science techniques for enhanced exploration, model benchmarking, and relationship discovery.
• Analyzing structural alternatives through multi-model and non-parametric approaches.
• Quantitatively incorporating societal preferences and their uncertainties using expected expected utility theory.
• Applying imprecise probabilities to address ambiguity in prior distributions for predictions and preferences.

Main Results:

• A novel framework combining diverse statistical techniques for comprehensive uncertainty quantification.
• Methods for explicitly considering uncertainties in both predictions and societal preferences.
• Guidelines for incorporating imprecise probabilities and uncertain preferences without significant computational increase.
• Demonstration of improved robustness in environmental decision support through advanced uncertainty analysis.

Conclusions:

• Comprehensive uncertainty quantification is essential for reliable environmental decision-making.
• Integrating advanced statistical and data science methods significantly improves uncertainty analysis.
• Addressing uncertainties in societal preferences and predictions leads to more robust environmental management strategies.
• The proposed methods provide a practical pathway for more complete uncertainty consideration in environmental decision support.