Risk assessment of a water supply system under climate variability: a stochastic approach
A model is developed to assess risk to a municipal water supply system under the influence of population growth and climate variability. To incorporate the uncertainty in water use, a model that combines time series Monte Carlo simulations and a deterministic artificial neural network (ANN) is developed to simulate the daily water demand under climate variation. The model is first applied to assess how climate change alters the risk of a current water supply system and is then used to estimate the effects of demand management programs and system expansion. The model quantifies water supply system risk in terms of reliability, resiliency, and vulnerability (RRV). The model evaluates 11 scenarios defined by combining various population growth forecasts, demand management programs, system expansions, and global climate model (GCM) scenarios. The simulation results suggest that a rise in temperature and a change in precipitation magnitude will negatively impact the performance of the case study system.