Parameter constraints in a stream ecosystem model: Incorporation of a priori information in Monte Carlo error analysis

Using a model of mass and nutrient dynamics in a stream, we evaluated the use of a priori information (e.g., mass balance constraints, limits for the state variables, etc.) to increase the accuracy and efficiency of Monte Carlo error analysis. The results indicated that a priori information can effectively eliminate physically unrealistic simulations and reduce uncertainty about the predicted values. Some types of a priori information proved more helpful than increased accuracy in measuring model parameters.