Future Research Challenges for Incorporating Uncertainty in Environmental and Ecological Decision-Making

Authors: Ascough Ii, James; MAIER, HOLGER - UNIVERSITY OF ADELAIDE; RAVALCO, JAKIN - UNIVERSITY OF ADELAIDE; Strudley, Mark

Submitted to: Ecological Modeling
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/5/2007
Publication Date: 12/3/2008
Citation: Ascough II, J.C., Maier, H.R., Ravalco, J.K., Strudley, M.W. 2008. Future Research Challenges for Incorporating Uncertainty in Environmental and Ecological Decision-Making. Ecological Modeling. 219(3-4):383-399.

Interpretive Summary: Environmental decision-making is extremely complex. Additional research is needed to acquire further knowledge and understanding of different types of uncertainty inherent in environmental decision-making, and how these areas of uncertainty affect the quality of decisions. Developing acceptable environmental decision-making approaches requires improvement of uncertainty analysis techniques, concepts, and assumptions in research. Some of the important issues that need to be addressed in relation to the incorporation of uncertainty in environmental decision-making processes include: 1) the development of methods for quantifying the uncertainty associated with human input; 2) the development of appropriate risk-based performance criteria that are understood and accepted by a range of disciplines; 3) improvement of fuzzy environmental decision-making through the development of hybrid approaches (e.g., fuzzy-rule-based models combined with probabilistic data-driven techniques); 4) development of methods for explicitly conveying uncertainties in environmental decision-making through the use of Bayesian probability theory; 5) incorporating adaptive management practices into the environmental decision-making process, including model divergence correction; 6) the development of approaches and strategies for increasing the computational efficiency of integrated models, optimization methods, and methods for estimating risk-based performance measures; and 7) the development of integrated frameworks for comprehensively addressing uncertainty as part of the environmental decision-making process.

Technical Abstract: Environmental decision-making is extremely complex due to the intricacy of the systems considered and the competing interests of multiple stakeholders. Additional research is needed to acquire further knowledge and understanding of different types of uncertainty (e.g., knowledge, variability, decision, and linguistic uncertainty) inherent in environmental decision-making, and how these areas of uncertainty affect the quality of decisions rendered. Developing acceptable and efficacious environmental decision-making approaches requires improvement of uncertainty analysis techniques, concepts, and assumptions in pertinent research, with subsequent implementation, monitoring and auditing, and possible modification of selected environmental management practices. Modelling and decision support tools (e.g., integrated assessment models, optimisation algorithms, and multicriteria decision analysis tools) are being used increasingly for comparative analysis and uncertainty assessment of environmental management alternatives. If such tools are to provide effective decision support, the uncertainties associated with all aspects of the decision-making process need to be explicitly considered. However, as models become more complex to better represent integrated environmental, social and economic systems, achieving this goal becomes more difficult. Some of the important issues that need to be addressed in relation to the incorporation of uncertainty in environmental decision-making processes include: 1) the development of methods for quantifying the uncertainty associated with human input; 2) the development of appropriate risk-based performance criteria that are understood and accepted by a range of disciplines; 3) improvement of fuzzy environmental decision-making through the development of hybrid approaches (e.g., fuzzy-rule-based models combined with probabilistic data-driven techniques); 4) development of methods for explicitly conveying uncertainties in environmental decision-making through the use of Bayesian probability theory; 5) incorporating adaptive management practices into the environmental decision-making process, including model divergence correction; 6) the development of approaches and strategies for increasing the computational efficiency of integrated models, optimization methods, and methods for estimating risk-based performance measures; and 7) the development of integrated frameworks for comprehensively addressing uncertainty as part of the environmental decision-making process.