W2: Sensitivity and Uncertainty Assessment of Integrated Environmental Models

This workshop provides a forum for: 1) increasing awareness of the significance of various sensitivity and uncertainty analysis techniques in the development and application of integrated environmental models; and 2) discussing and critically evaluating the contribution of these techniques to improved modeling of environmental systems. Objectives include communicating state-of-the-art information on sensitivity and uncertainty methodologies, and identifying research directions and potential collaborations for improving these methods in the context of integrated environmental modeling.

Suitable topics for this workshop include, but are not limited to:

Sensitivity Analysis

1. The use of sensitivity analysis to gain insights into key sources of uncertainty that should be priorities for additional data collection or research.
2. Key criteria in selecting methods of sensitivity assessment for different model structures and modeling problems.
3. Practical strategies for sensitivity analysis given models with large parameter sets or high computing loads.
4. Sensitivity analysis in the context of probabilistic risk assessment (PRA).
5. (a) Limitations of current sensitivity analysis methods in environmental science; and (b) Promising directions (e.g., neural network approaches) for improving methods of sensitivity analysis.

Uncertainty Analysis

1. Evaluation of uncertainty in model outputs with respect to decision making or risk management objectives.
2. Uncertainty propagation in complex, environmental model with large parameter sets or high computing loads.
3. Selection and estimation of appropriate risk-based performance measures in relation to measuring sustainability.
4. Incorporation of uncertainty in decision support methods, such as multi-criteria decision analysis.
5. Assessment of political and socio-economic uncertainties, including investigation (from a social science perspective) of how models/decisions are structured or conceptualised.
6. Comparison of the magnitude and sources of uncertainty inherent in economic models (e.g., water demand forecast, contingent valuation models) with those in integrated environmental models.
7. Development and evaluation of uncertainty analysis methods that appropriately consider subjective and qualitative factors.
8. Assessing and quantifying information requirements (e.g., theories, data, models) to reduce predictive uncertainty in environmental models.
9. Scale effects in uncertainty assessment of integrated environmental models.
10. Methods for identifying and managing structural uncertainty and bias in integrated environmental models
11. (a) Limitations of current uncertainty analysis methods in environmental science; and (b) Promising directions (e.g., neural network approaches) for improving methods of uncertainty analysis.

Go To Workshop Blog

Position Papers
H.R. Maier and J.C. Ascough II.	Uncertainty in Environmental Decision-Making: Issues, Challenges and Future Directions
J. Mysiak and J. D.Brown	Environmental Policy Aid Under Uncertainty
Participants
Katherine von Stackelberg	Integrated Risk Modeling Frameworks: A Case Study of Exposure to Polychlorinated Biphenyls
Katherine von Stackelberg	Village Level Tactics for the Control of Parasitic Disease: An Example at the Intersection of Control Engineering and Bayesian Analysis
Jaroslav Mysiak, James Brown	Uncertainty and policy support
Shuguang Liu, Pamela Anderson, Guoyi Zhou, Boone Kauffman, Flint Hughes, David Schimel, Vicente Watson and Joseph Tosi	Resolving Model Parameter Values From C and N Stock Measurements in a Wide Range of Tropical Mature Forests Using Nonlinear Inversion
Huub Scholten	Modelling Support To Improve The Quality Of Environmental Studies