In environmental engineering, identification of problems and their solutions as well as the identification of the relevant processes involved is often done by means of case study analyses. By researching the operation of urban drainage and water distribution systems, this methodology is suited to evaluate new technologies, strategies or measures with regard to their impact on the overall processes. However, data availability is often limited and data collection and the development of new models are both costly and time consuming. Hence, new technologies, strategies or measures can only be tested on a limited number of case studies. In several environmental disciplines a few virtual case studies have been manually developed to provide data for research tasks and these are repeatedly used in different research projects. Efforts have also been invested in tackling limited data availability with the algorithmic generation of virtual case studies having constant or varying boundary conditions. The data provided by such tools is nevertheless only available for a certain instance in time. With DynaVIBe (Dynamic Virtual Infrastructure Benchmarking), numerous virtual case studies are algorithmically generated with a temporal development of the urban structure (population and land use model) and infrastructure. This provides a methodology that allows for the analysis of future scenarios on a spatio-temporal city scale. By linking a population model with DynaVIBe's infrastructure models, socio-economics impacts on infrastructure and system coherences can be investigated. The problematic of limited case study data is solved by the algorithmic generation of an unlimited number of virtual case studies, which are dynamic over time. Additionally, this methodology can also be applied on real world data for probabilistic future scenario analysis.
Keywords: socio-economic impacts, stochastic scenario analysis, total urban water cycle, virtual case studies