A stochastic dynamic constituent transport model was developed, capable of simulating the operation of a water distribution system containing pumps and storage tanks, and subject to random demands and contaminant inputs. Long term operation of a hypothetical small town water supply system containing one pump station and one storage tank was simulated while the system was subjected to external contaminant inputs. Repeated simulations were made under different regimes of external contamination applied to the tank, the pump station and at system nodes, and internal contamination representing biofilm effects based on assumed relationships between flow velocities and bioflim cell detachment. Seven sampling plans representing regulatory requirements and industry practice were applied during the simulation to evaluate their ability to detect the contamination under a presence/absence criterion. The simulations were able to identify contamination patterns and provide information useful in the definition of sampling plans. Time of sampling was found to be as important as location. This was true both within the monitoring period, and particularly within the diurnal cycle of demand. Spreading samples over different days within the monitoring period rather than sampling all on one day, always improved contaminant detection. Detection by plans based on fixed times and locations were very sensitive to those times and locations. There was no best plan suitable for all situations tested. The better sampling plans were those that captured the temporal and spatial contamination patterns present in the system. No consistent advantage was noted from sampling in proportion to population served or in locating sampling nodes systematically instead of randomly. The location and timing of sampling for most plans could be improved with the knowledge of actual contamination patterns and timing provided by the model. The presence of a storage tank was found to have a strong influence on hydraulic patterns and the location and timing of contamination reaching different parts of the system. / Ph. D.