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.

The practice of rainwater harvesting (RWH) has been studied extensively in recent years, as it has the potential to alleviate some of the increasing stress on urban water distribution systems and drainage networks. Within the field, an approach of real-time control of rainwater storage is emerging as a method to improve the ability of RWH systems to reduce runoff and urban drainage flows. As applying real-time control on RWH tanks means releasing water that could be used for supply, applying controlled-release policies often hinders the RWH system’s ability to supply water. The suggested study presents an approach that has the potential to improve the capability of a distributed network of RWH systems to mitigate peak drainage flows while substantially reducing the impact on harvested rainwater availability. The suggested method uses a genetic algorithm to generate release policies, which are tailored for any given rain event and initial conditions. The algorithm utilizes the modeled drainage system’s response to a given rainfall pattern and manages to substantially reduce peak drainage flows with little impact on available rainwater when compared to the conventional no-release alternative and other active release methods.