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 typ...

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Bibliographic Details
Published in:Environmental modelling & software : with environment data news 2020-01, Vol.123, p.104532, Article 104532
Main Authors: Torres, María Nariné, Fontecha, John E., Zhu, Zhenduo, Walteros, Jose L., Rodríguez, Juan Pablo
Format: Article
Language:English
Subjects:
Computer applications
Decision making
Drainage systems
Drinking water
Geographic information systems
Irrigation water
Iterative methods
Linear programming
Minimum cost
Mixed integer
Mixed integer program
Optimization
Participatory approach
Pollutants
Pollution load
Rain water
Rainfall
Rainwater harvesting
Rainwater recovery systems
Remote sensing
Runoff
Runoff volume
Sewage
Sewer systems
Stochasticity
Storm runoff
Stormwater
Stormwater management
Stormwater recycling
Sustainable harvest
Sustainable urban drainage systems
Two-stage stochastic programming
Urban drainage
Urban environments
Water harvesting
Water pollution
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Summary: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. •A flexible computational framework to select and allocate SUDS was developed.•The objectives are minimizing the use of potable water for irrigation and reducing the water runoff at a minimum cost.•A Geographic Information System (GIS) is coupled with a two-stage stochastic mixed integer linear program (TS-MILP).•The proposed methods can be easily adapted to incorporate a wide variety of stakeholder requirements or preferences.•An iterative participatory approach is used to include stakeholders in the decision-making process.
ISSN:1364-8152
1873-6726
DOI:10.1016/j.envsoft.2019.104532