To assess the uncertainty of model predictions, Monte Carlo simulation (MCS) is commonly applied. However, when modeling water distribution system quality under unsteady conditions, MCS computation times can be excessive even for a reasonably sized system. The aim of this study is to evaluate alternative estimation schemes and examine their ability to predict model prediction uncertainty with less computational effort. Here, MCS results are compared with a point estimation method, the first order second moment (FOSM) method, and a quasi-MCS method, Latin hypercube sampling (LHS). Hydraulic and water quality simulations are performed using EPANET for a typical pressure zone sized system with 116 pipes and 90 nodes. The primary model outputs of interest are chlorine concentrations and water age. Nodal pressures are also evaluated. Preliminary analysis showed that these outputs are most sensitive to nodal demands of all system parameters thus only demand uncertainty results are presented. Results demonstrate that LHS provides very good estimates of the predicted means and variances for steady and unsteady conditions compared with MCS while FOSM did well for steady conditions but did poorly for some periods in the extended period simulation.