Urban trees represent a natural, yet underutilized, resource for sustainable stormwater management. This paper presents RainwaterHarvestingApp, an open-source Python tool designed to simulate, analyze, and optimize rainwater harvesting systems integrated with individual urban trees. The software combines three-dimensional parametric modeling of tree geometry with a component-based hydrological simulation to quantify rainfall interception and evaluate the performance of engineered conical collectors. An integrated optimization routine identifies design parameters that achieve theoretically high collection efficiencies under its simplified assumptions, often exceeding 90% in simulations, thereby substantially reducing potential runoff. It is designed as a conceptual design and educational tool for exploring system performance and trade-offs. The tool guides users through an interactive workflow from parametric input and rainfall definition to simulation, optimization, and dynamic visualization. In a baseline case, an optimized cone radius of 3.19 m captured 1.5985 m³ of a 1.7671 m³ rainfall event, representing a 34.9% efficiency gain over a non-optimized design. These results represent idealized, upper-bound estimates based on the model's current simplifications. By bridging interception theory with practical design exploration, the tool provides a reproducible, accessible platform for the component-level planning of tree-based rainwater harvesting, supporting the development of more resilient urban water strategies.
Arganis et al. (Tue,) studied this question.