Metabolism-modelling approaches to long-term sustainability assessment of urban water services

There is a discernible need for a holistic, long-term and sustainability approach in decision-making in water and wastewater utilities around the world. Metabolism-based modelling, which can quantify various flows within an urban water system (UWS), has shown its effective usability for a more comprehensive understanding of the impacts of intervention strategies and can be used by any water utility for future planning of UWS. This study presents the main principles of a holistic Sustainability Assessment Framework which can be simulated by using two analytical, conceptual, mass-balance-based models to quantify relevant key performance indicators (KPIs) associated with the metabolic flows of the urban water cycle. These two models are WaterMet² (WM2) and dynamic metabolism model (DMM), developed recently under the aegis of the EU TRUST (Transitions to the Urban Water Services of Tomorrow) project. There are clear differences between the two models which make them useful in different contexts and circumstantial situations. DMM is a mass-balance consistent model which quantifies and presents annually-aggregated performance values for system wide energy consumption, emissions, environmental impacts and costs for the entire UWS though it is also possible to derive corresponding indicators for individual sub-systems (e.g. water distribution and wastewater transport). WM2 is the opposite of this, it is a distributed metabolism model which simulates water related and other resource flows throughout the UWS components with a higher resolution both spatially (e.g. multiple water resources and service reservoirs) and temporally (e.g. daily and monthly), and thereby is useful in contexts where utilities would like to focus on further details of the UWS metabolism with the aim to understand and solve specific problems. Overall, these two complementary metabolism-based approaches enable any water utility to quantitatively explore and understand the influences of different external drivers and intervention strategies on future performance profiles linked to any physical, environmental and economic criteria.