The combined actions of natural and human factors change the timing and availability of water resources and, correspondingly, water demand in metropolitan areas. This leads to an imbalance between supply and demand and, thus, an increase in the vulnerability of water supply systems. Accordingly, methods for systematic analysis and multifactor assessment are needed to estimate the vulnerability of individual components in an integrated water supply system. This paper introduces a new approach to comprehensively assess vulnerability by integrating water resource system characteristics with factors representing exposure, sensitivity, severity, potential severity, social vulnerability, and adaptive capacity. These factors provide a way to consider broader system elements beyond the traditional vulnerability evaluation methods solely on the basis of the magnitude of failure (i.e., severity). In this way, the new vulnerability index gives a more detailed assessment with the potential to recognize critical conditions and components in an integrated system. The effectiveness and advantages of the proposed approach are checked using an investigation of the water supply system of Salt Lake City (SLC), Utah. First, an integrated water resource model was developed using a system simulation software to allocate water from different sources in SLC among designated demand points. The model contains individual simulation modules with representative interconnections among the natural hydroclimate system, built water infrastructure, and institutional decision making. The results of the analysis illustrate that basing vulnerability on a sole factor may lead to insufficient understanding and, hence, inefficient management of the system. For example, ranking of different water sources on the basis of the traditional vulnerability index (i.e., severity) in SLC is not consistent with the ranking on the basis of the proposed integrated vulnerability index. Therefore, during a failure event in the system, such as a water shortage, incomplete understanding of the system's performance may lead to incorrect decisions by managers. The new vulnerability index and assessment approach was able to identify the most vulnerable water sources in the SLC integrated water supply system. In conclusion, use of a more comprehensive approach to simulate the system behavior and estimate vulnerability provides more guidance for decision makers to detect vulnerable components of the system and ameliorate decision making.

Abstract:The Social Vulnerability Index (SoVI) has served the hazards community well for more than a decade. Using Utah as a test case, a state with a population exposed to a variety of hazards, this study sought to build upon the SoVI approach by augmenting it with a non-linear Artificial Neural Network (ANN). A SoVI was created for the state of Utah at the census block group level using five-year data (2008)(2009)(2010)(2011)(2012) from the American Community Survey. The SoVI provided a dataset from which to train a neural network. The ANN was then used to classify a subset of the state to determine if it could provide a comparable classification of vulnerability. The ANN produced a vulnerability classification that was approximately 26% consistent with the SoVI created using the traditional approach. The differences in classifications were assessed using radar plots of block group variable averages to explore how the variables were handled in each classification. The results of this study warrant further investigation of the capabilities of an ANN-enhanced SoVI.