Integrated risk assessment of urban water supply systems from source to tap

Abstract

Urban water supply systems (UWSS) are generally composed of water sources, transmission pipes, treatment plants, and distribution networks from source to tap and usually are exposed to variety of uncertain threatening hazards. These threats can be divided to three main groups of natural, human-made, and operational hazards which affect either water quantity or water quality. In order to evaluate the reliability of water supply systems, risk assessment tools must be used to identify threats, their probability, and consequences and vulnerabilities of each element of these systems against the hazards. Due to the complexity and uncertainties affecting water supply systems and threatening hazards, a comprehensive and effective risk assessment method is required. In this study, an integrated fuzzy hierarchical risk assessment model for water supply systems (IFHRA-WSS) is proposed to assess hazards in a complex UWSS using a systematic approach incorporating both water quantity and quality issues. This model uses a hierarchical framework for breaking down the UWSS infrastructures to their interrelated elements to reduce the overall complexity of the system. It also considers uncertainties using Fuzzy Logic approach. Effects of functional interdependencies between different components of the system have also been considered in the vulnerability analysis. IFHRA-WSS incorporates the contributions of urban water experts in a group risk assessment procedure in a way that they can be easily expressed in terms of the qualitative and quantitative risk measures. Efficiency of this model has been examined in a case study which includes a large part of a drinking water supply system in a major city in Iran. This system includes all the elements of the UWSS from the delivery point to the consumption point. In the case study, different components and subcomponents of this system have been ranked based on their estimated risk values. It is envisaged that the results of the proposed model can help the decision makers to plan for effective risk mitigation measures.