With sustainable development as the new overarching goal, urban wastewater system (UWS) managers are now being asked to take all social, economic, technical and environmental facets related to their decisions into account. In this complex decision-making environment, uncertainty can be formidable. By instance, demand can increase/decrease due to changes in population, more restrictive environmental discharge requirements can be adopted, or new sewage processes can be integrated.  This leads to the conclusion that better decisions will be made if the decision-making process were adaptive and iterative. Although UWS decision-support frameworks exist in the literature, but none of them effectively addresses all these needs.

The thesis “Decision-support for adaptive and sustainable urban wastewater system management in the face of uncertainty”, by Antonia Hadjimichael describes such a conceptual framework. It can be used to assess environmental and socio-economic impacts of UWS management options under various conditions, both present and future. This is achieved by i) establishing an adaptive management process for decision-support that evaluates and compares alternative solutions, ii) using robustness, reliability and resilience measures to evaluate the performance of the system, and iii) including a valuation uncertainty analysis that incorporates uncertain valuation assumptions in the decision-making process.

The researcher applied the framework to two illustrative case studies: the Congost UWS in Catalonia, Spain and UWS of Eindhoven and the Dommel River in the Netherlands. The Congost UWS represents a typical problem faced by many managers: poor river water quality, an increasing population and more stringent water quality legislation. The following tools were utilised: i) a Cost Benefit Analysis (CBA) including monetised environmental benefits and damages; ii) a Robustness Analysis of system performance against potential future conditions; iii) Reliability and Resilience Analyses of the system given contextual variability; and iv) a Valuation Uncertainty Analysis of model parameters. Regarding the case study of Eindhoven UWS, four options for upgrading the UWS were assessed against the base-case “do-nothing” option. The aim was to reduce the overall environmental impact by targeting river dissolved oxygen (DO) depletion and ammonia peaks, reducing combined sewer overflows (CSOs) and enhancing nutrient removal. Both applications have demonstrated novel integrations of metrics and methods that will be valuable for UWS analysis and future work.