Integrated modelling of risk and uncertainty underlying the cost and effectiveness of water quality measures

In this paper we present an overview of the most important sources of uncertainty when analysing the least cost way to improve water quality. The estimation of the cost-effectiveness of water quality measures is surrounded by environmental, economic and political uncertainty. These different types of uncertainty are identified, quantified and analysed with the help of a risk and uncertainty model written in Excel Visual Basic using Monte Carlo simulation. The novelty of the work is that we investigate the combined effect of uncertainty in both the cost and effect assessment in a probabilistic way as a logical extension of traditional approaches to uncertainty analysis like sensitivity and scenario analysis. The model provides insight into the robustness of the ranking of water quality measures by explicating the probability that one measure is more cost-effective than another, applying different distributional assumptions. We show that the interaction between environmental and economic uncertainty is not straightforward. In the presented case study, the level of uncertainty in the cost-effectiveness indicator can be approximated by the highest uncertainty value in either the cost or effectiveness estimate.