A participatory approach based on stochastic optimization for the spatial allocation of Sustainable Urban Drainage Systems for rainwater harvesting.

Rainwater Harvesting (RWH) is the practice of capturing and storing stormwater for later use. In addition to being an alternative source of water for non-potable applications, RWH is also used to effectively reduce stormwater runoff volumes and pollutant loads dropped into sewage systems. RWH is typically carried out by placing a variety of Sustainable Urban Drainage Systems (SUDS) in different locations of the urban landscape. However, because of the staggering number of potential combinations of SUDS typologies and spatial configurations that can be used, identifying a strategy that optimally selects and allocates SUDS to maximize the benefits of RWH is a complex endeavor. One of the challenges that emerges during the optimal design and location of these systems is incorporating the inherent uncertainty of the rainfall. In this paper, we develop a flexible computational framework that couples a Geographic Information System (GIS) with a two-stage stochastic mixed integer linear program (TS-MILP) to select and locate SUDS in order to minimize the use of potable water for irrigation and reduce the water runoff at a minimum cost. This framework incorporates an iterative participatory approach to engage stakeholders in the decision-making process. We tested the proposed methodology on a case study for the central campus at Universidad de Los Andes (Bogotá, Colombia). Our results showed that the expected value of the total runoff volume and the consumption of potable water can be reduced up to 67% and 50%, respectively.