The objective of the presented study was to develop a high-temporal-resolution stochastic rainwater harvesting (RWH) model for assessing the dual benefits of RWH: potable water savings and runoff reduction. Model inputs of rainfall and water demand are used in a stochastic manner, maintaining their natural pattern, while generating realistic noise and temporal variability. The dynamic model solves a mass-balance equation for the rainwater tank, while logging all inflows and outflows from it for post-simulation analysis. The developed model can simulate various building sizes, roof areas, rainwater tank volumes, controlled release policies, and time periods, providing a platform for assessing short- and long-term benefits. Standard passive rainwater harvesting operation and real-time control policies (controlled release) are demonstrated for a 40-apartment building with rainfall data typical for a Mediterranean climate, showing the system’s ability to supply water for non-potable uses, while reducing runoff volumes and flows, with the latter significantly improved when water is intentionally released from the tank prior to an expected overflow. The model could be used to further investigate the effects of rainwater harvesting on the urban water cycle, by coupling it with an urban drainage model and simulating the operation of a distributed network of micro-reservoirs that supply water and mitigate floods.
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