Decision Making Under Uncertainty and Complexity

The City of Monterrey in Nuevo Leon is rapidly increasing its demand for potable water due to its growing industrial activity and population. It is widely believed that the expansion of the city´s water infrastructure is a key measure needed to support future water demand. However, environmental concerns of different projects and more importantly climate change and water demand uncertainty have increased the complexity of this decision.

This research describes an integrated computational framework that has been developed for supporting the State of Nuevo Leon’s water infrastructure decisions. This framework uses conjunctively three different computational models: a water demand Monte-Carlo simulator, a water supply hydrological model and a dynamic optimization model. This framework is used in a computational experiment that uses a large ensemble of future scenarios exploring a vast space of water demand and water supply scenarios. The resulting database future scenarios is then analysed using statistical clustering algorithms to identify the factors that increase or reduce the vulnerability of different infrastructure portfolios. Finally, this vulnerability assessment is used to developed adaptive infrastructure investment plans. Our results show future water demand in the city can be met progressively through a combination of different projects. In the short term, small-to-medium scale grey infrastructure that take advantage of different water sources (i.e. surface and groundwater sources) can be used to meet future demand in the face of climate uncertainty. In the medium term, the combination of water efficiency and medium size grey infrastructure projects can help the city meet future demand and save close to 1 billion dollars in infrastructure investments.