Risk Assessment of a Water Supply System under Climate Variability: A Stochastic Approach

Abstract

In this study, a model is developed to assess risk to a municipal water supply system under the influence of population growth and climate change. To incorporate the uncertainly in water use, a model which combines time series Monte Carlo simulations and a deterministic artificial neural network (ANN) is developed to simulate the daily water demand under climate variability.

The model is then expanded in two directions. One direction is to estimate the effects of demand management programs and system expansion on the reliability, resiliency, and vulnerability of the water supply system. Another direction is to capture the possible impacts of climate change on the risk of a water supply system. Twenty-six scenarios generated from different combinations of demand management programs, system expansions and Global Climate Model (GCM) scenarios were set to illustrate the risk indices: reliability, resiliency, and vulnerability. To illustrate the effects of a change of precipitation frequency and a higher population growth, twenty-five additional scenarios were evaluated.