Following the events of September 11, 2001, in the United States, world public awareness for possible terrorist attacks on water supply systems has increased dramatically. Among the different threats for a water distribution system, the most difficult to address is a deliberate chemical or biological contaminant injection, due to both the uncertainty of the type of injected contaminant and its consequences, and the uncertainty of the time and location of the injection. An online contaminant monitoring system is considered as a major opportunity to protect against the impacts of a deliberate contaminant intrusion. However, although optimization models and solution algorithms have been developed for locating sensors, little is known about how these design algorithms compare to the efforts of

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[1] Automatic calibration routines for hydrologic models with multiple objective capabilities are becoming increasingly popular due to advances in computational power, population-based optimization techniques, and the recognition that a single performance measure such as the root-mean-square error is no longer sufficient to characterize the complex behavior of the catchment. However, as more objective functions are included in the calibration, the number of Pareto-optimal solutions as well as the number of ''near'' Pareto-optimal parameter sets increases. The calibration problem quickly becomes a decision-making problem. In the practical sense, users of automatic calibration routines have to face the task of selecting a set of suitable model parameters from the numerous Pareto-optimal sets. A new method of automatic calibration is proposed, which combines an effective optimization routine, based on multiobjective genetic algorithms, and Pareto preference ordering. In this case, Pareto-optimal points that are also Pareto-optimal in different subspace combinations of the objective functions space are preferred. The proposed method is used to calibrate the MIKE11/NAM rainfall-runoff model for a Danish catchment. The results indicated that the method is able to sieve through the numerous Pareto-optimal solutions and select a small number of preferred solutions. This is extremely useful to modelers who typically are required to provide the best estimated parameter sets with good overall model performance.