Traditionally, environmental decision analysis in subsurface contamination scenarios is performed using cost -benefit analysis. In this paper, we discuss some of the limitations associated with cost -benefit analysis, especially its definition of risk, its definition of cost of risk, and its poor ability to communicate risk-related information. This paper presents an integrated approach for management of contaminated ground water resources using health risk assessment and economic analysis through a multi-criteria decision analysis framework. The methodology introduces several important concepts and definitions in decision analysis related to subsurface contamination. These are the trade-off between population risk and individual risk, the trade-off between the residual risk and the cost of risk reduction, and cost-effectiveness as a justification for remediation. The proposed decision analysis framework integrates probabilistic health risk assessment into a comprehensive, yet simple, cost-based multi-criteria decision analysis framework. The methodology focuses on developing decision criteria that provide insight into the common questions of the decision-maker that involve a number of remedial alternatives. The paper then explores three potential approaches for alternative ranking, a structured explicit decision analysis, a heuristic approach of importance of the order of criteria, and a fuzzy logic approach based on fuzzy dominance and similarity analysis. Using formal alternative ranking procedures, the methodology seeks to present a structured decision analysis framework that can be applied consistently across many different and complex remediation settings. A simple numerical example is presented to demonstrate the proposed methodology. The results showed the importance of using an integrated approach for

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[1] The impact of inaccurate or unreliable hydrological data on model calibration can be significant. A special case is when calibration data are from different sources with varying accuracy, e.g., missing streamflow measurements that have been supplemented by interpolation or extrapolation. Since the relative accuracy of data is commonly defined subjectively, model calibration with data of varying uncertainty is difficult. In this work, a framework using the method of order of importance is presented to incorporate soft information to describe the relative accuracy of calibration data. A numerical example using synthetic data is presented to demonstrate the validity of the methodology. The applicability of the framework is demonstrated for the Fishtrap Creek Catchment of Washington State, where the short streamflow record with significant gaps was reconstructed using support vector machines (SVM). Incorporation of this educated judgment about the relative accuracy of the calibration data resulted in identification of faulty model calibration as well as errors in the SVM prediction of extreme streamflow events, which would have been undetected otherwise. The methodology was further applied to two management scenarios involving flood frequency analysis and satisfying legally established in-stream flow requirements.

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[1] Allocation of pollution control responsibilities is an important problem in environmental economics. Previous research has developed the least-cost formulation that addresses both cost efficiency and hydrologic uncertainty. However, the least-cost formulation does not explicitly consider the distribution of pollution control responsibilities that has a serious implication on equity. A theoretical framework for evaluating the trade-offs between cost efficiency and equity is developed in this work. In this paper the efficiency equity curve that defines the cost of achieving equity is developed. It also evaluates the impacts of hydrologic variability and parameter uncertainty on the trade-off between cost efficiency and equity, and the impacts of the choice of equity criteria on phosphorus management decisions. A demonstration example is also presented that analyzed phosphorus management in the Fishtrap Creek catchment and the lower Nooksack River basin located in northwestern Washington State.Citation: Khadam, I. M., and J. J. Kaluarachchi (2006), Trade-offs between cost minimization and equity in water quality management for agricultural watersheds, Water Resour. Res., 42, W10404,