Publication: Source to tap urban water cycle modelling
Post date: Jul 16, 2014 6:42:05 PM
Rozos, E., and C. Makropoulos
The continuous expansion of urban areas is associated with increased water demand, both for domestic and non-domestic uses. To cover this additional demand, centralised infrastructure, such as water supply and distribution networks tend to become more and more complicated and are eventually over-extended with adverse effects on their reliability. To address this, there exist two main strategies: (a) Tools and algorithms are employed to optimise the operation of the external water supply system, in an effort to minimise risk of failure to cover the demand (either due to the limited availability of water resources or due to the limited capacity of the transmission system and treatment plants) and (b) demand management is employed to reduce the water demand per capita. Dedicated tools do exist to support the implementation of these two strategies separately. However, there is currently no tool capable of handling the complete urban water system, from source to tap, allowing for an investigation of these two strategies at the same time and thus exploring synergies between the two. This paper presents a new version of the UWOT model (Makropoulos et al., 2008), which adopts a metabolism modelling approach and is now capable of simulating the complete urban water cycle from source to tap and back again: the tool simulates the whole water supply network from the generation of demand at the household level to the water reservoirs and tracks wastewater generation from the household through the wastewater system and the treatment plants to the water bodies. UWOT functionality is demonstrated in the case of the water system of Athens and outputs are compared against the current operational tool used by the Water Company of Athens. Results are presented and discussed: The discussion highlights the conditions under which a single source-to-tap model is more advantageous than dedicated subsystem models.
Environmental Modelling and Software, 41, 139–150, doi:10.1016/j.envsoft.2012.11.015, Elsevier, 1 March 2013.